WGCNA GO Analysis

sessionInfo() #provides list of loaded packages and version of R. 
## R version 4.3.2 (2023-10-31)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.0
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## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
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## [17] bslib_0.6.1       yaml_2.3.8        rlang_1.1.3       jsonlite_1.8.8

First, load the necessary packages.

library(goseq)
## Loading required package: BiasedUrn
## Loading required package: geneLenDataBase
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library(dplyr)
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library(forcats)
library(ggplot2)
library(gridExtra)
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library(tidyr)
library(grDevices)
library(reshape2)
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library(Rmisc)
## Loading required package: lattice
## Loading required package: plyr
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library(ggpubr)
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library(tibble)
library(gridExtra)
library(tidyr)
library(zoo)
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library(ComplexHeatmap)
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## ========================================
## ComplexHeatmap version 2.16.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
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## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
##     genomic data. Bioinformatics 2016.
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## The new InteractiveComplexHeatmap package can directly export static 
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
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library(circlize)
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## circlize version 0.4.15
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
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## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
##   in R. Bioinformatics 2014.
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library(GSEABase)
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## Loading required package: annotate
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library(stringr)
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library(GenomicRanges)
## Loading required package: GenomeInfoDb
library(rtracklayer)
library(rrvgo)

Load in data

library(rtracklayer)
gff<-rtracklayer::import("../../data/Pocillopora_acuta_HIv2.genes_fixed.gff3")
gff<-as.data.frame(gff)
dim(gff) # 478988     9
## [1] 478988     13
names(gff) 
##  [1] "seqnames"      "start"         "end"           "width"        
##  [5] "strand"        "source"        "type"          "score"        
##  [9] "phase"         "ID"            "transcript_id" "gene_id"      
## [13] "Parent"
transcripts <- subset(gff, type == "transcript")
transcripts_gr <- makeGRangesFromDataFrame(transcripts, keep.extra.columns=TRUE) #extract length information
transcript_lengths <- width(transcripts_gr) #isolate length of each gene
seqnames<-transcripts_gr$ID #extract list of gene id 
lengths<-cbind(seqnames, transcript_lengths)
lengths<-as.data.frame(lengths) #convert to data frame

dim(transcripts) #33730    13
## [1] 33730    13
kegg <- read.delim("../../data/Pocillopora_acuta_HIv2.genes.KEGG_results.txt",header = FALSE)
kegg <- as.data.frame(kegg)
colnames(kegg)[1] <- "gene_id" 
colnames(kegg)[2] <- "KEGG_new"
head(kegg)
##                                      gene_id KEGG_new
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a         
## 2 Pocillopora_acuta_HIv2___RNAseq.g24143.t1b   K22584
## 3  Pocillopora_acuta_HIv2___RNAseq.g22918.t1         
## 4  Pocillopora_acuta_HIv2___RNAseq.g18333.t1   K03386
## 5   Pocillopora_acuta_HIv2___RNAseq.g7985.t1         
## 6  Pocillopora_acuta_HIv2___RNAseq.g13511.t1
eggnog<-read.delim("../../data/Pocillopora_acuta_HIv2.genes.EggNog_results.txt")#this file contains all of the go terms, descriptions, kegg, etc
eggnog<- plyr::rename(eggnog, c("X.query"="gene_id"))
head(eggnog,2)
##                                      gene_id  seed_ortholog    evalue score
## 1 Pocillopora_acuta_HIv2___RNAseq.g24143.t1a 45351.EDO48725 2.16e-120   364
## 2  Pocillopora_acuta_HIv2___RNAseq.g18333.t1 45351.EDO38694 3.18e-123   355
##                                                                           eggNOG_OGs
## 1 COG0620@1|root,KOG2263@2759|Eukaryota,38GZS@33154|Opisthokonta,3BNKS@33208|Metazoa
## 2 COG0450@1|root,KOG0852@2759|Eukaryota,38B9P@33154|Opisthokonta,3BGS4@33208|Metazoa
##   max_annot_lvl COG_category
## 1 33208|Metazoa            E
## 2 33208|Metazoa            O
##                                           Description Preferred_name
## 1    Cobalamin-independent synthase, Catalytic domain              -
## 2 negative regulation of male germ cell proliferation          PRDX4
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         GOs
## 1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         -
## 2 GO:0000003,GO:0001775,GO:0002252,GO:0002263,GO:0002274,GO:0002275,GO:0002283,GO:0002366,GO:0002376,GO:0002443,GO:0002444,GO:0002446,GO:0003006,GO:0003674,GO:0003824,GO:0004601,GO:0005488,GO:0005515,GO:0005575,GO:0005576,GO:0005615,GO:0005622,GO:0005623,GO:0005737,GO:0005783,GO:0005790,GO:0005829,GO:0006082,GO:0006457,GO:0006464,GO:0006468,GO:0006520,GO:0006575,GO:0006793,GO:0006796,GO:0006807,GO:0006810,GO:0006887,GO:0006915,GO:0006950,GO:0006952,GO:0006955,GO:0006979,GO:0007154,GO:0007165,GO:0007249,GO:0007252,GO:0007275,GO:0007276,GO:0007283,GO:0007548,GO:0008150,GO:0008152,GO:0008219,GO:0008285,GO:0008379,GO:0008406,GO:0008584,GO:0009056,GO:0009266,GO:0009409,GO:0009605,GO:0009607,GO:0009617,GO:0009628,GO:0009636,GO:0009893,GO:0009966,GO:0009967,GO:0009987,GO:0010467,GO:0010604,GO:0010646,GO:0010647,GO:0010941,GO:0010942,GO:0010950,GO:0010952,GO:0012501,GO:0012505,GO:0016043,GO:0016192,GO:0016209,GO:0016310,GO:0016491,GO:0016684,GO:0016999,GO:0017001,GO:0017144,GO:0019222,GO:0019471,GO:0019538,GO:0019725,GO:0019752,GO:0019953,GO:0022414,GO:0022417,GO:0023051,GO:0023052,GO:0023056,GO:0030141,GO:0030162,GO:0030198,GO:0031323,GO:0031325,GO:0031410,GO:0031974,GO:0031982,GO:0031983,GO:0032268,GO:0032270,GO:0032501,GO:0032502,GO:0032504,GO:0032940,GO:0033554,GO:0034774,GO:0035556,GO:0036211,GO:0036230,GO:0042119,GO:0042127,GO:0042221,GO:0042592,GO:0042737,GO:0042742,GO:0042743,GO:0042744,GO:0042802,GO:0042803,GO:0042981,GO:0043062,GO:0043065,GO:0043067,GO:0043068,GO:0043085,GO:0043170,GO:0043207,GO:0043226,GO:0043227,GO:0043229,GO:0043231,GO:0043233,GO:0043280,GO:0043281,GO:0043299,GO:0043312,GO:0043412,GO:0043436,GO:0043900,GO:0043901,GO:0044093,GO:0044237,GO:0044238,GO:0044248,GO:0044260,GO:0044267,GO:0044281,GO:0044421,GO:0044422,GO:0044424,GO:0044433,GO:0044444,GO:0044446,GO:0044464,GO:0044703,GO:0045055,GO:0045137,GO:0045321,GO:0045454,GO:0045862,GO:0046425,GO:0046427,GO:0046546,GO:0046661,GO:0046903,GO:0046983,GO:0048232,GO:0048513,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048608,GO:0048609,GO:0048731,GO:0048856,GO:0050789,GO:0050790,GO:0050794,GO:0050896,GO:0051171,GO:0051173,GO:0051179,GO:0051186,GO:0051187,GO:0051234,GO:0051239,GO:0051241,GO:0051246,GO:0051247,GO:0051336,GO:0051345,GO:0051604,GO:0051704,GO:0051707,GO:0051716,GO:0051920,GO:0052547,GO:0052548,GO:0055114,GO:0060205,GO:0060255,GO:0061458,GO:0065007,GO:0065008,GO:0065009,GO:0070013,GO:0070417,GO:0070887,GO:0071704,GO:0071840,GO:0072593,GO:0080090,GO:0097190,GO:0097237,GO:0097708,GO:0098542,GO:0098754,GO:0098869,GO:0099503,GO:0101002,GO:1901564,GO:1901605,GO:1902531,GO:1902533,GO:1904813,GO:1904892,GO:1904894,GO:1905936,GO:1905937,GO:1990748,GO:2000116,GO:2000241,GO:2000242,GO:2000254,GO:2000255,GO:2001056,GO:2001233,GO:2001235,GO:2001267,GO:2001269
##          EC   KEGG_ko
## 1  2.1.1.14 ko:K00549
## 2 1.11.1.15 ko:K03386
##                                                                           KEGG_Pathway
## 1 ko00270,ko00450,ko01100,ko01110,ko01230,map00270,map00450,map01100,map01110,map01230
## 2                                                                     ko04214,map04214
##   KEGG_Module KEGG_Reaction             KEGG_rclass
## 1      M00017 R04405,R09365 RC00035,RC00113,RC01241
## 2           -             -                       -
##                             BRITE KEGG_TC CAZy BiGG_Reaction
## 1 ko00000,ko00001,ko00002,ko01000       -    -             -
## 2 ko00000,ko00001,ko01000,ko04147       -    -             -
##                 PFAMs
## 1         Meth_synt_2
## 2 1-cysPrx_C,AhpC-TSA
gogene <- merge(transcripts, eggnog, by=c("gene_id"), all=T)
gogene <- merge(gogene, kegg, by=c("gene_id"), all=T)
head(gogene,2)
##                                   gene_id                           seqnames
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t Pocillopora_acuta_HIv2___Sc0000013
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t Pocillopora_acuta_HIv2___Sc0000013
##     start     end width strand   source       type score.x phase
## 1 4542087 4551503  9417      + AUGUSTUS transcript      NA    NA
## 2 4639103 4647350  8248      + AUGUSTUS transcript      NA    NA
##                                        ID
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t
##                             transcript_id Parent       seed_ortholog   evalue
## 1 Pocillopora_acuta_HIv2___RNAseq.10002_t             45351.EDO27354 2.41e-93
## 2 Pocillopora_acuta_HIv2___RNAseq.10010_t        6087.XP_002166004.2 1.28e-38
##   score.y
## 1     317
## 2     164
##                                                                           eggNOG_OGs
## 1 COG0666@1|root,KOG0510@2759|Eukaryota,38G7Q@33154|Opisthokonta,3BCDU@33208|Metazoa
## 2                                              COG0666@1|root,KOG4177@2759|Eukaryota
##    max_annot_lvl COG_category                                Description
## 1  33208|Metazoa           DZ osmolarity-sensing cation channel activity
## 2 2759|Eukaryota            I                           spectrin binding
##   Preferred_name
## 1          TRPA1
## 2              -
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## 1 GO:0000302,GO:0001580,GO:0002791,GO:0002793,GO:0003008,GO:0003012,GO:0003674,GO:0004888,GO:0005034,GO:0005215,GO:0005216,GO:0005217,GO:0005244,GO:0005245,GO:0005261,GO:0005262,GO:0005488,GO:0005515,GO:0005575,GO:0005623,GO:0005886,GO:0005887,GO:0006810,GO:0006811,GO:0006812,GO:0006816,GO:0006873,GO:0006874,GO:0006875,GO:0006936,GO:0006939,GO:0006950,GO:0006979,GO:0007154,GO:0007165,GO:0007166,GO:0007204,GO:0007600,GO:0007602,GO:0007606,GO:0007610,GO:0007638,GO:0008150,GO:0008324,GO:0009266,GO:0009314,GO:0009408,GO:0009409,GO:0009410,GO:0009416,GO:0009453,GO:0009581,GO:0009582,GO:0009583,GO:0009593,GO:0009605,GO:0009612,GO:0009628,GO:0009636,GO:0009719,GO:0009966,GO:0009967,GO:0009987,GO:0010033,GO:0010035,GO:0010037,GO:0010243,GO:0010378,GO:0010646,GO:0010647,GO:0010817,GO:0014070,GO:0014074,GO:0014832,GO:0014848,GO:0015075,GO:0015085,GO:0015267,GO:0015276,GO:0015278,GO:0015318,GO:0016020,GO:0016021,GO:0016043,GO:0016048,GO:0016324,GO:0019233,GO:0019722,GO:0019725,GO:0019932,GO:0022607,GO:0022803,GO:0022832,GO:0022834,GO:0022836,GO:0022838,GO:0022839,GO:0022843,GO:0022857,GO:0022890,GO:0023041,GO:0023051,GO:0023052,GO:0023056,GO:0030001,GO:0030003,GO:0030424,GO:0031000,GO:0031224,GO:0031226,GO:0031644,GO:0031646,GO:0032024,GO:0032421,GO:0032501,GO:0032879,GO:0032880,GO:0032991,GO:0033554,GO:0033555,GO:0034220,GO:0034605,GO:0034702,GO:0034703,GO:0035556,GO:0035690,GO:0035774,GO:0036270,GO:0038023,GO:0040011,GO:0040040,GO:0042221,GO:0042330,GO:0042331,GO:0042391,GO:0042493,GO:0042542,GO:0042592,GO:0042752,GO:0042802,GO:0042995,GO:0043005,GO:0043052,GO:0043269,GO:0043270,GO:0043279,GO:0043933,GO:0044057,GO:0044070,GO:0044085,GO:0044425,GO:0044459,GO:0044464,GO:0045177,GO:0046677,GO:0046873,GO:0046883,GO:0046887,GO:0046957,GO:0048265,GO:0048518,GO:0048519,GO:0048522,GO:0048523,GO:0048583,GO:0048584,GO:0048878,GO:0050708,GO:0050714,GO:0050789,GO:0050794,GO:0050796,GO:0050801,GO:0050848,GO:0050850,GO:0050877,GO:0050896,GO:0050906,GO:0050907,GO:0050909,GO:0050912,GO:0050913,GO:0050951,GO:0050954,GO:0050955,GO:0050960,GO:0050961,GO:0050965,GO:0050966,GO:0050968,GO:0050974,GO:0050982,GO:0051046,GO:0051047,GO:0051049,GO:0051050,GO:0051179,GO:0051209,GO:0051222,GO:0051223,GO:0051234,GO:0051239,GO:0051240,GO:0051259,GO:0051260,GO:0051262,GO:0051282,GO:0051283,GO:0051289,GO:0051480,GO:0051606,GO:0051641,GO:0051649,GO:0051716,GO:0051930,GO:0051931,GO:0051969,GO:0052129,GO:0055065,GO:0055074,GO:0055080,GO:0055082,GO:0055085,GO:0060089,GO:0060341,GO:0060401,GO:0060402,GO:0061178,GO:0065003,GO:0065007,GO:0065008,GO:0070201,GO:0070417,GO:0070588,GO:0070838,GO:0070887,GO:0071241,GO:0071244,GO:0071310,GO:0071312,GO:0071313,GO:0071407,GO:0071415,GO:0071417,GO:0071466,GO:0071495,GO:0071840,GO:0071944,GO:0072347,GO:0072503,GO:0072507,GO:0072511,GO:0090087,GO:0090276,GO:0090277,GO:0097458,GO:0097553,GO:0097603,GO:0097604,GO:0098590,GO:0098655,GO:0098660,GO:0098662,GO:0098771,GO:0098796,GO:0098862,GO:0098900,GO:0098908,GO:0099094,GO:0099604,GO:0120025,GO:1901698,GO:1901699,GO:1901700,GO:1901701,GO:1902495,GO:1902531,GO:1902533,GO:1903522,GO:1903530,GO:1903532,GO:1903793,GO:1904058,GO:1904951,GO:1990351,GO:1990760
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##   EC   KEGG_ko     KEGG_Pathway KEGG_Module KEGG_Reaction KEGG_rclass
## 1  - ko:K04984 ko04750,map04750           -             -           -
## 2  -         -                -           -             -           -
##                     BRITE                                 KEGG_TC CAZy
## 1 ko00000,ko00001,ko04040 1.A.4.6.1,1.A.4.6.2,1.A.4.6.3,1.A.4.6.5    -
## 2                       -                                       -    -
##   BiGG_Reaction                           PFAMs KEGG_new
## 1             - Ank,Ank_2,Ank_3,Ank_4,Ion_trans   K04984
## 2             - Ank,Ank_2,Ank_4,Ank_5,Ion_trans   K04984
geneInfo <- read.csv("../../output/WGCNA/WGCNA_ModuleMembership.csv") #this file was generated from the WGCNA analyses and contains the modules of interest
geneInfo<- plyr::rename(geneInfo, c("X"="gene_id"))
dim(geneInfo) # there are 9012 genes in our gene info file
## [1] 9012   40
geneInfo <- merge(gogene, geneInfo, by=c("gene_id"), all=T)

Format GO terms to remove dashes and quotes and separate by semicolons (replace , with ;) in GOs column

geneInfo$GOs <- gsub(",", ";", geneInfo$GOs)
geneInfo$GOs <- gsub('"', "", geneInfo$GOs)
geneInfo$GOs <- gsub("-", NA, geneInfo$GOs)

geneInfo$KEGG_new[geneInfo$KEGG_new == ""] <- NA
unique(geneInfo$moduleColor)
##  [1] "green"        NA             "blue"         "salmon"       "turquoise"   
##  [6] "yellow"       "black"        "red"          "magenta"      "lightcyan"   
## [11] "purple"       "brown"        "pink"         "midnightblue" "tan"         
## [16] "cyan"
geneInfo$Length<-lengths$transcript_lengths[match(geneInfo$gene_id, lengths$seqnames)]

Run the GOSeq function by module color to test for GO term enrichment. Due to high number of enriched GO terms, I am using an adjusted p-value threshold of <0.001 and using rrvgo package to reduce redundancy in list of GO terms.

Calcification

### Generate vector with names of all genes 
ALL.vector <- c(geneInfo$gene_id)
### Generate length vector for all genes 
LENGTH.vector <- as.integer(geneInfo$Length)
calc_up_mods <- c("brown", "red", "black", "pink", "salmon", "blue")

nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")) #942
## [1] 942
nrow(geneInfo %>% dplyr::filter(moduleColor=="red")) #425
## [1] 425
nrow(geneInfo %>% filter(moduleColor=="black")) #396
## [1] 396
nrow(geneInfo %>% filter(moduleColor=="pink")) #220
## [1] 220
nrow(geneInfo %>% filter(moduleColor=="salmon")) #154
## [1] 154
nrow(geneInfo %>% filter(moduleColor=="blue")) #1989
## [1] 1989
sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="brown")), nrow(geneInfo %>% dplyr::filter(moduleColor=="red")), nrow(geneInfo %>% filter(moduleColor=="black")), nrow(geneInfo %>% filter(moduleColor=="pink")), nrow(geneInfo %>% filter(moduleColor=="salmon")), nrow(geneInfo %>% filter(moduleColor=="blue")))
## [1] 4126
# 4126

calc_down_mods <- c("turquoise","magenta","lightcyan")

nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")) #2558
## [1] 2558
nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")) #219
## [1] 219
nrow(geneInfo %>% filter(moduleColor=="lightcyan")) #65
## [1] 65
sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="turquoise")), nrow(geneInfo %>% dplyr::filter(moduleColor=="magenta")), nrow(geneInfo %>% filter(moduleColor=="lightcyan")))
## [1] 2842
# 2842

other_mods <- c("green","yellow", "purple", "midnightblue","cyan","tan")

sum(nrow(geneInfo %>% dplyr::filter(moduleColor=="green")), nrow(geneInfo %>% dplyr::filter(moduleColor=="yellow")), nrow(geneInfo %>% filter(moduleColor=="purple")), nrow(geneInfo %>% filter(moduleColor=="midnightblue")), nrow(geneInfo %>% filter(moduleColor=="cyan")),nrow(geneInfo %>% filter(moduleColor=="tan")))
## [1] 2044
# 2044

# 4126 + 2842 + 2044 = 9012, which represents all of our genes

4126 genes are in the 6 modules significantly upregulated by calcification.

### Generate vector with names in just the module we are analyzing
# ID.vector <- geneInfo %>%
#   filter(moduleColor==c("brown", "red", "black", "pink", "salmon", "green")) %>%
#   #get_rows(.data[[module]]))%>%
#   pull(gene_id)

ID.vector <- geneInfo %>%
  filter(moduleColor %in% c("brown", "red", "black", "pink", "salmon", "blue")) %>%
  pull(gene_id)

length(ID.vector) #4126
## [1] 4126
##Get a list of GO Terms for each module
GO.terms <- geneInfo %>%
  filter(moduleColor %in% c("brown", "red", "black", "pink", "salmon", "blue")) %>%
  #filter(get_rows(.data[[module]]))%>%
  dplyr::select(GOs,gene_id) %>% rename(GOs = "GO.terms")

dim(GO.terms) #4126    2
## [1] 4126    2
##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";") 
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split)) 
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2
##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector) 
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene 
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) 
## Warning in pcls(G): initial point very close to some inequality constraints

#run goseq using Wallenius method for all categories of GO terms 
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)
## Using manually entered categories.
## Calculating the p-values...
## 'select()' returned 1:1 mapping between keys and columns
GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"
#adjust p-values 
GO$bh_adjust <-  p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values
#Filtering for p < 0.01
GO <- GO %>%
        dplyr::filter(bh_adjust<0.00001) %>%
        dplyr::arrange(., ontology, bh_adjust)
   
#Write file of results 
write.csv(GO, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")
#add vector for terms of interest to reduce number of GO terms - NOT using this to look at individual modules for exploratory purposes
keywords<-c("metabolism", "carbon","bicarbonate", "apoptosis", "death", "symbiosis", "regulation of cell communication", "trans membrane transport", "transmembrane",  "organic substance transport", "inorganic substance transport","response to stress", "antioxidant", "calcification","biomineralization", "heat","transporters","proton transport","ion transport","acid-base", "homeostasis")
go_results <- read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification.csv")
 go_results<-go_results%>%
      filter(ontology=="BP")%>%
      filter(bh_adjust != "NA") %>%
      filter(numInCat>10)%>%
      arrange(., bh_adjust)
dim(go_results)
## [1] 2774    9
head(go_results)
##   X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0000003                       0                        1        465
## 2 2 GO:0006139                       0                        1        608
## 3 3 GO:0006355                       0                        1        462
## 4 4 GO:0006725                       0                        1        650
## 5 5 GO:0006807                       0                        1       1215
## 6 6 GO:0006810                       0                        1        664
##   numInCat                                             term ontology bh_adjust
## 1      465                                     reproduction       BP         0
## 2      608 nucleobase-containing compound metabolic process       BP         0
## 3      462        regulation of DNA-templated transcription       BP         0
## 4      650     cellular aromatic compound metabolic process       BP         0
## 5     1215              nitrogen compound metabolic process       BP         0
## 6      664                                        transport       BP         0
library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package 
simMatrix <- calculateSimMatrix(go_results$GOterm,
                                orgdb="org.Ce.eg.db", #c. elegans database
                                ont="BP",
                                method="Rel")
## 
## preparing gene to GO mapping data...
## preparing IC data...
 #calculate similarity 
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Ce.eg.db")
dim(reducedTerms)
## [1] 2213   10
#keep only the goterms from the reduced list
go_results<-go_results%>%
  filter(GOterm %in% reducedTerms$go)
 #add in parent terms to list of go terms 
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
write.csv(go_results, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")
#plot significantly enriched GO terms by Slim Category faceted by slim term 
 GO.plot_calcification <-  ggplot(go_results, aes(x = ontology, y = term)) + 
    geom_point(aes(size=bh_adjust)) + 
    scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
    facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
    theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
    strip.text.y = element_text(angle=0, size = 10),
    strip.text.x = element_text(size = 20),
    axis.text = element_text(size = 8),
    axis.title.x = element_blank(),
    axis.title.y = element_blank())
GO.plot_calcification

ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification.png", plot=GO.plot_calcification, dpi=300, height=100, units="in", limitsize=FALSE)
## Saving 7 x 100 in image

Count number of GOterms by ParentTerm for the upregulation of calcification

library(dplyr)

result <- go_results %>%
  dplyr::group_by(ParentTerm) %>%
  dplyr::summarize(Number.of.terms = n_distinct(term))%>%
  mutate(Calcification.direction = "Up")

print(result)
## # A tibble: 149 × 3
##    ParentTerm                             Number.of.terms Calcification.direct…¹
##    <chr>                                            <int> <chr>                 
##  1 DNA metabolic process                               26 Up                    
##  2 DNA-templated transcription initiation               6 Up                    
##  3 RNA processing                                      12 Up                    
##  4 actin filament-based process                        12 Up                    
##  5 aging                                                2 Up                    
##  6 amide metabolic process                             12 Up                    
##  7 ammonium ion metabolic process                       1 Up                    
##  8 anatomical structure morphogenesis                  27 Up                    
##  9 animal organ development                            15 Up                    
## 10 behavior                                            10 Up                    
## # ℹ 139 more rows
## # ℹ abbreviated name: ¹​Calcification.direction

Calcification - down

2842 genes are in the 4 modules significantly downregulated by calcification.

### Generate vector with names of all genes 
ALL.vector <- c(geneInfo$gene_id)
### Generate length vector for all genes 
LENGTH.vector <- as.integer(geneInfo$Length)
### Generate vector with names in just the module we are analyzing
ID.vector <- geneInfo%>%
  filter(moduleColor %in% c("turquoise","magenta","lightcyan"))%>%
  pull(gene_id)

length(ID.vector) #2842
## [1] 2842
##Get a list of GO Terms for each module
GO.terms <- geneInfo%>%
  filter(moduleColor %in% c("turquoise","magenta","lightcyan"))%>%
  dplyr::select(GOs,gene_id) %>% rename(GOs = "GO.terms")

dim(GO.terms) #2842    2
## [1] 2842    2
##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms$GO.terms), ";") 
split2 <- data.frame(v1 = rep.int(GO.terms$gene, sapply(split, length)), v2 = unlist(split)) 
colnames(split2) <- c("gene", "GO.terms")
GO.terms<-split2
##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector) 
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene 
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) 
## Warning in pcls(G): initial point very close to some inequality constraints

#run goseq using Wallenius method for all categories of GO terms 
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)
## Using manually entered categories.
## Calculating the p-values...
## 'select()' returned 1:1 mapping between keys and columns
GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"
#adjust p-values 
GO$bh_adjust <-  p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values
#Filtering for p < 0.01
GO <- GO %>%
        dplyr::filter(bh_adjust<0.00001) %>%
        dplyr::arrange(., ontology, bh_adjust)
   
#Write file of results 
write.csv(GO, file = "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")
go_results <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down.csv")
go_results<-go_results%>%
      filter(ontology=="BP")%>%
      filter(bh_adjust != "NA") %>%
      filter(numInCat>10)%>%
      arrange(., bh_adjust)

dim(go_results)
## [1] 1892    9
head(go_results)
##   X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0006139                       0                        1        485
## 2 2 GO:0006725                       0                        1        513
## 3 3 GO:0006807                       0                        1        910
## 4 4 GO:0006810                       0                        1        419
## 5 5 GO:0006950                       0                        1        364
## 6 6 GO:0006996                       0                        1        461
##   numInCat                                             term ontology bh_adjust
## 1      485 nucleobase-containing compound metabolic process       BP         0
## 2      513     cellular aromatic compound metabolic process       BP         0
## 3      910              nitrogen compound metabolic process       BP         0
## 4      419                                        transport       BP         0
## 5      364                               response to stress       BP         0
## 6      461                           organelle organization       BP         0
library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package 
simMatrix <- calculateSimMatrix(go_results$GOterm,
                                orgdb="org.Ce.eg.db", #c. elegans database
                                ont="BP",
                                method="Rel")
## preparing gene to GO mapping data...
## preparing IC data...
 #calculate similarity 
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Ce.eg.db")
dim(reducedTerms)
## [1] 1638   10
#keep only the goterms from the reduced list
go_results<-go_results%>%
  filter(GOterm %in% reducedTerms$go)
 #add in parent terms to list of go terms 
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
write.csv(go_results, "../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")
#plot significantly enriched GO terms by Slim Category faceted by slim term 
 GO.plot_calcification_down <-  ggplot(go_results, aes(x = ontology, y = term)) + 
    geom_point(aes(size=bh_adjust)) + 
    scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
    facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
    theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
    strip.text.y = element_text(angle=0, size = 10),
    strip.text.x = element_text(size = 20),
    axis.text = element_text(size = 8),
    axis.title.x = element_blank(),
    axis.title.y = element_blank())
GO.plot_calcification_down

ggsave(filename="../../output/WGCNA/GO_analysis/GO.plot_calcification_down.png", plot=GO.plot_calcification_down, dpi=300, height=100, units="in", limitsize=FALSE)
## Saving 7 x 100 in image

Count number of GOterms by ParentTerm for the downregulation of calcification

library(dplyr)

result_down <- go_results %>%
  dplyr::group_by(ParentTerm) %>%
  dplyr::summarize(Number.of.terms = n_distinct(term))%>%
  mutate(Calcification.direction = "Down")

print(result_down)
## # A tibble: 128 × 3
##    ParentTerm                             Number.of.terms Calcification.direct…¹
##    <chr>                                            <int> <chr>                 
##  1 RNA processing                                      29 Down                  
##  2 actin filament-based process                        11 Down                  
##  3 aging                                                2 Down                  
##  4 amide metabolic process                             12 Down                  
##  5 ammonium ion metabolic process                       1 Down                  
##  6 anatomical structure morphogenesis                  29 Down                  
##  7 animal organ development                            16 Down                  
##  8 behavior                                             8 Down                  
##  9 biological process involved in inters…               7 Down                  
## 10 biosynthetic process                                24 Down                  
## # ℹ 118 more rows
## # ℹ abbreviated name: ¹​Calcification.direction

code for by module

library(rrvgo)

# Define the unique module colors
module_colors <- na.omit(unique(geneInfo$moduleColor))

# Generate vector with names of all genes 
ALL.vector <- c(geneInfo$gene_id)

# Generate length vector for all genes 
LENGTH.vector <- as.integer(geneInfo$Length)

 
# Loop over each unique module color
for (color in module_colors) {
 # Filter geneInfo based on the current color
  color_filtered <- geneInfo %>% filter(moduleColor == color)

  # Generate vector with names in just the module we are analyzing
  ID.vector <- color_filtered$gene_id
  
  length(ID.vector)

  # Get a list of GO Terms for each module
  GO.terms <- color_filtered %>%
    dplyr::select(GOs, gene_id) %>%
    rename(GOs = "GO.terms")
  
  dim(GO.terms)

  ## Format to have one GO term per row with gene ID repeated
  split <- strsplit(as.character(GO.terms$GO.terms), ";") 
  split2 <- data.frame(v1 = rep.int(GO.terms$gene_id, sapply(split, length)), v2 = unlist(split)) 
  colnames(split2) <- c("gene", "GO.terms")
  GO.terms <- split2

  ## Construct list of genes with 1 for genes in module and 0 for genes not in the module
  gene.vector <- as.integer(ALL.vector %in% ID.vector) 
  names(gene.vector) <- ALL.vector # set names
  # Weight gene vector by bias for length of gene 
  pwf <- nullp(gene.vector, ID.vector, bias.data = LENGTH.vector) 

  # Run goseq using Wallenius method for all categories of GO terms 
  GO.wall <- goseq(pwf, ID.vector, gene2cat = GO.terms, test.cats = c("GO:BP", "GO:MF", "GO:CC"), method = "Wallenius", use_genes_without_cat = TRUE)
  GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
  colnames(GO)[1] <- "GOterm"

  # Adjust p-values 
  GO$bh_adjust <- p.adjust(GO$over_represented_pvalue, method = "BH") 

  # Filtering for p < 0.01
  GO <- GO %>%
    dplyr::filter(bh_adjust < 0.00001) %>%
    dplyr::arrange(., ontology, bh_adjust)
   
  # Write file of results 
  write.csv(GO, file = paste0("../../output/WGCNA/GO_analysis/goseq_pattern_", color, ".csv"))

  go_results <- GO

  go_results<-go_results%>%
      filter(ontology=="BP")%>%
      filter(bh_adjust != "NA") %>%
      filter(numInCat>100)%>%
      arrange(., bh_adjust)
  
  #Reduce/collapse GO term set with the rrvgo package 
  simMatrix <- calculateSimMatrix(go_results$GOterm,
                                orgdb="org.Ce.eg.db", #c. elegans database
                                ont="BP",
                                method="Rel")
  
   #calculate similarity 
  scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
  reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Ce.eg.db")
  
  #keep only the goterms from the reduced list
  go_results <- go_results %>% filter(GOterm %in% reducedTerms$go)
  
  #add in parent terms to list of go terms 
  go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]
  
  #plot significantly enriched GO terms by Slim Category faceted by slim term 
 GO.plot <-  ggplot(go_results, aes(x = ontology, y = term)) + 
    geom_point(aes(size=bh_adjust)) + 
    scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
    facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
    theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
    strip.text.y = element_text(angle=0, size = 10),
    strip.text.x = element_text(size = 20),
    axis.text = element_text(size = 8),
    axis.title.x = element_blank(),
    axis.title.y = element_blank())

 GO.plot
 
  length(colnames(go_results)[go_results$ParentTerm=="cation transport"])
  length(colnames(go_results)[go_results$ParentTerm=="inorganic ion homeostasis"])
  length(colnames(go_results)[go_results$ParentTerm=="regulation of cellular response to stress"])
}

Specific differentially expressed genes

wgcna_counts_filtered<-read.csv("../../output/Filtered_gene_count_matrix.csv", strip.white=T)
wgcna_counts_filtered<- plyr::rename(wgcna_counts_filtered, c("X"="Gene"))
colnames(wgcna_counts_filtered)
##  [1] "Gene"  "RF13B" "RF13D" "RF14B" "RF14C" "RF15B" "RF15D" "RF17B" "RF17D"
## [10] "RF18B" "RF18D" "RF19B" "RF19C" "RF20B" "RF20C" "RF22B" "RF22C" "RF23A"
## [19] "RF23C" "RF24B" "RF24D" "RF25A" "RF25C" "RS11B" "RS11D" "RS12A" "RS12C"
## [28] "RS13A" "RS13C" "RS14B" "RS14C" "RS15B" "RS15D" "RS1B"  "RS1C"  "RS2B" 
## [37] "RS2C"  "RS3B"  "RS3D"  "RS6A"  "RS6D"  "RS7B"  "RS7C"  "RS8B"  "RS8C" 
## [46] "RS9A"  "RS9C"
library(tidyr)
wgcna_counts_filtered_long <- pivot_longer(wgcna_counts_filtered, cols=2:47, names_to = "Colony", values_to = "Counts")
wgcna_counts_filtered_long$Colony <- as.factor(wgcna_counts_filtered_long$Colony)
head(wgcna_counts_filtered_long)
## # A tibble: 6 × 3
##   Gene                                      Colony Counts
##   <chr>                                     <fct>   <int>
## 1 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF13B      61
## 2 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF13D      73
## 3 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF14B      62
## 4 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF14C      51
## 5 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF15B      31
## 6 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF15D      37
wgcna_counts_filtered_long <- wgcna_counts_filtered_long %>% 
  separate(Colony, into = c('Origin', 'Colony.number'), sep = 2)
head(wgcna_counts_filtered_long)
## # A tibble: 6 × 4
##   Gene                                      Origin Colony.number Counts
##   <chr>                                     <chr>  <chr>          <int>
## 1 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     13B               61
## 2 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     13D               73
## 3 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     14B               62
## 4 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     14C               51
## 5 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     15B               31
## 6 Pocillopora_acuta_HIv2___RNAseq.g27841.t1 RF     15D               37
library(stringr)
wgcna_counts_filtered_long$Colony <- as.numeric(str_extract(wgcna_counts_filtered_long$Colony.number, "[0-9]+"))
wgcna_counts_filtered_long<-wgcna_counts_filtered_long %>% 
   mutate(Treatment = trimws(str_remove(wgcna_counts_filtered_long$Colony.number, "(\\s+[A-Za-z]+)?[0-9-]+")))
head(wgcna_counts_filtered_long)
## # A tibble: 6 × 6
##   Gene                              Origin Colony.number Counts Colony Treatment
##   <chr>                             <chr>  <chr>          <int>  <dbl> <chr>    
## 1 Pocillopora_acuta_HIv2___RNAseq.… RF     13B               61     13 B        
## 2 Pocillopora_acuta_HIv2___RNAseq.… RF     13D               73     13 D        
## 3 Pocillopora_acuta_HIv2___RNAseq.… RF     14B               62     14 B        
## 4 Pocillopora_acuta_HIv2___RNAseq.… RF     14C               51     14 C        
## 5 Pocillopora_acuta_HIv2___RNAseq.… RF     15B               31     15 B        
## 6 Pocillopora_acuta_HIv2___RNAseq.… RF     15D               37     15 D
wgcna_counts_filtered_long$Origin <- as.factor(wgcna_counts_filtered_long$Origin)
wgcna_counts_filtered_long$Treatment <- as.factor(wgcna_counts_filtered_long$Treatment)
wgcna_counts_filtered_long <- wgcna_counts_filtered_long %>%
  mutate(Treatment2 = ifelse(Treatment == "A" | Treatment == "B", "Variable",
               ifelse(Treatment == "C" | Treatment == "D", "Stable", NA)))
wgcna_counts_filtered_long$Treatment2 <- as.factor(wgcna_counts_filtered_long$Treatment2)

SLC4A7

wgcna_counts_filtered_long_SLC4A7<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7402.t1")
library(nlme)
## 
## Attaching package: 'nlme'
## The following object is masked from 'package:IRanges':
## 
##     collapse
## The following object is masked from 'package:dplyr':
## 
##     collapse
library(emmeans)
SLC4A7.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_SLC4A7, na.action=na.exclude)
car::Anova(SLC4A7.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      11.9704  1  0.0005405 ***
## Origin            0.4869  1  0.4853006    
## Treatment         1.0445  3  0.7904802    
## Origin:Treatment  1.3516  3  0.7169263    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(SLC4A7.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        654 61.5 19      525      783
##  RS        453 54.2 19      339      566
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      201 80.8 19   2.491  0.0221
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
SLC4A7_sum<-summarySE(wgcna_counts_filtered_long_SLC4A7, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
SLC4A7_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 710.3636 265.8632 80.16078 178.6093
## 2     RF   Variable 11 609.8182 241.3930 72.78272 162.1700
## 3     RS     Stable 12 463.8333 248.6454 71.77773 157.9817
## 4     RS   Variable 12 469.8333 166.4407 48.04730 105.7514

Figure

pd<- position_dodge(0.2)
SLC4A7_fig<-ggplot(data=SLC4A7_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_SLC4A7,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(SLC4A7~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
SLC4A7_fig

##SLC4A3

wgcna_counts_filtered_long_SLC4A3 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g27873.t1")
wgcna_counts_filtered_long_SLC4A3 
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               52     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               65     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              105     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               52     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               22     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               50     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               44     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               94     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               58     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               60     18 D         Stable    
## # ℹ 36 more rows
SLC4A3.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_SLC4A3 , na.action=na.exclude)
car::Anova(SLC4A3.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      24.6847  1  6.752e-07 ***
## Origin            1.7488  1     0.1860    
## Treatment         0.4857  3     0.9220    
## Origin:Treatment  1.1274  3     0.7705    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(SLC4A3.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       73.1 5.98 19     60.6     85.6
##  RS       58.5 5.28 19     47.4     69.6
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     14.6 7.81 19   1.866  0.0776
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
SLC4A3_sum<-summarySE(wgcna_counts_filtered_long_SLC4A3 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
SLC4A3_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 70.63636 21.65767 6.530032 14.54982
## 2     RF   Variable 11 71.72727 27.65896 8.339491 18.58154
## 3     RS     Stable 12 61.50000 22.71763 6.558016 14.43410
## 4     RS   Variable 12 56.33333 17.55166 5.066726 11.15179

Figure

pd<- position_dodge(0.2)
SLC4A3_fig<-ggplot(data=SLC4A3_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_SLC4A3 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(SLC4A3 ~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
SLC4A3_fig

##NHE3

wgcna_counts_filtered_long_NHE3<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g24868.t1")
wgcna_counts_filtered_long_NHE3
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               88     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               93     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              143     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               96     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              123     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              136     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               87     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              111     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               95     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               87     18 D         Stable    
## # ℹ 36 more rows
NHE3.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_NHE3, na.action=na.exclude)
car::Anova(NHE3.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      45.5717  1  1.472e-11 ***
## Origin            1.1334  1     0.2870    
## Treatment         5.3488  3     0.1480    
## Origin:Treatment  5.9678  3     0.1132    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(NHE3.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        111 6.90 19     96.9      126
##  RS        102 6.19 19     89.0      115
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     9.42 8.58 19   1.098  0.2861
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
NHE3_sum<-summarySE(wgcna_counts_filtered_long_NHE3, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
NHE3_sum
##   Origin Treatment2  N    Counts       sd        se       ci
## 1     RF     Stable 11 105.18182 15.16455  4.572284 10.18768
## 2     RF   Variable 11 121.63636 33.62818 10.139278 22.59172
## 3     RS     Stable 12 106.66667 22.81281  6.585491 14.49457
## 4     RS   Variable 12  97.83333 22.00757  6.353040 13.98295

Figure

pd<- position_dodge(0.2)
NHE3_fig<-ggplot(data=NHE3_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_NHE3,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(NHE3~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
NHE3_fig

##CA1

wgcna_counts_filtered_long_CA1<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g12304.t1")
wgcna_counts_filtered_long_CA1
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B             2854     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D             4635     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             2949     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C             4681     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             7704     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D             8665     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             3948     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D             3887     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             6896     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             6597     18 D         Stable    
## # ℹ 36 more rows
CA1.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_CA1, na.action=na.exclude)
car::Anova(CA1.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                   Chisq Df Pr(>Chisq)   
## (Intercept)      7.3722  1   0.006624 **
## Origin           0.7737  1   0.379079   
## Treatment        2.6212  3   0.453784   
## Origin:Treatment 2.3227  3   0.508194   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(CA1.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean  SE df lower.CL upper.CL
##  RF       5178 542 19     4045     6312
##  RS       2568 479 19     1565     3571
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS     2611 704 19   3.709  0.0015
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
CA1_sum<-summarySE(wgcna_counts_filtered_long_CA1, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA1_sum
##   Origin Treatment2  N   Counts       sd       se        ci
## 1     RF     Stable 11 5970.636 2621.084 790.2864 1760.8679
## 2     RF   Variable 11 4733.455 1986.062 598.8204 1334.2549
## 3     RS     Stable 12 2653.583 1847.177 533.2340 1173.6400
## 4     RS   Variable 12 2775.250 1463.636 422.5154  929.9501

Figure

pd<- position_dodge(0.2)
CA1_fig<-ggplot(data=CA1_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_CA1,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CA1~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
CA1_fig

##CA2

wgcna_counts_filtered_long_CA2<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g13824.t1")
wgcna_counts_filtered_long_CA2
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              139     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              164     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              169     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              249     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              174     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              232     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              232     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              204     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              244     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              338     18 D         Stable    
## # ℹ 36 more rows
CA2.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_CA2, na.action=na.exclude)
car::Anova(CA2.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      12.1927  1  0.0004798 ***
## Origin            8.6459  1  0.0032780 ** 
## Treatment         2.6667  3  0.4459141    
## Origin:Treatment  1.7689  3  0.6217181    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(CA2.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF     148.05 16.4 19    113.8    182.3
##  RS      -4.05 15.0 19    -35.5     27.4
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      152 18.2 19   8.346  <.0001
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
CA2_sum<-summarySE(wgcna_counts_filtered_long_CA2, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA2_sum
##   Origin Treatment2  N     Counts        sd        se        ci
## 1     RF     Stable 11 150.181818 98.679094 29.752866 66.293518
## 2     RF   Variable 11 131.272727 70.816793 21.352066 47.575369
## 3     RS     Stable 12  10.583333  9.894519  2.856302  6.286678
## 4     RS   Variable 12   9.916667  8.106769  2.340223  5.150795

Figure

pd<- position_dodge(0.2)
CA2_fig<-ggplot(data=CA2_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_CA2,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CA2~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
CA2_fig

##VHA

wgcna_counts_filtered_long_VHA<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g23064.t1")
wgcna_counts_filtered_long_VHA
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               28     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               19     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               26     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               21     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               35     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               30     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               30     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               21     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               16     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               17     18 D         Stable    
## # ℹ 36 more rows
VHA.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_VHA, na.action=na.exclude)
car::Anova(VHA.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      17.3800  1   3.06e-05 ***
## Origin            0.5371  1     0.4636    
## Treatment         2.5674  3     0.4632    
## Origin:Treatment  2.9805  3     0.3946    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(VHA.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       24.6 2.29 19     19.8     29.3
##  RS       24.9 2.01 19     20.7     29.1
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS   -0.353 3.02 19  -0.117  0.9080
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
VHA_sum<-summarySE(wgcna_counts_filtered_long_VHA, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
VHA_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 21.54545  5.317210 1.603199 3.572151
## 2     RF   Variable 11 27.36364  5.427204 1.636364 3.646045
## 3     RS     Stable 12 26.75000 13.784873 3.979350 8.758491
## 4     RS   Variable 12 23.41667  7.025387 2.028054 4.463718

Figure

pd<- position_dodge(0.2)
VHA_fig<-ggplot(data=VHA_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_VHA,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(VHA~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
VHA_fig

##HSP90

wgcna_counts_filtered_long_HSP90<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g6656.t1")
wgcna_counts_filtered_long_HSP90
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              908     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              778     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             1301     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              891     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             1043     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              948     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             1547     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              906     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             1425     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             1568     18 D         Stable    
## # ℹ 36 more rows
HSP90.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HSP90, na.action=na.exclude)
car::Anova(HSP90.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      34.6376  1  3.972e-09 ***
## Origin            0.5404  1     0.4623    
## Treatment         2.0956  3     0.5528    
## Origin:Treatment  0.7946  3     0.8508    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(HSP90.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       1345 92.2 19     1152     1538
##  RS       1102 80.8 19      933     1271
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS      243 123 19   1.985  0.0618
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
HSP90_sum<-summarySE(wgcna_counts_filtered_long_HSP90, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HSP90_sum
##   Origin Treatment2  N    Counts       sd        se       ci
## 1     RF     Stable 11 1218.1818 382.7918 115.41607 257.1630
## 2     RF   Variable 11 1426.5455 363.5721 109.62111 244.2511
## 3     RS     Stable 12  969.3333 311.6707  89.97157 198.0261
## 4     RS   Variable 12 1274.5833 397.4287 114.72777 252.5141

Figure

pd<- position_dodge(0.2)
HSP90_fig<-ggplot(data=HSP90_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_HSP90,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(HSP90~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
HSP90_fig

##HIF1A

wgcna_counts_filtered_long_HIF1A <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g3039.t1")
wgcna_counts_filtered_long_HIF1A 
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              172     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              158     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               99     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              159     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              127     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              164     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               93     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              128     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              104     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              213     18 D         Stable    
## # ℹ 36 more rows
HIF1A.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HIF1A , na.action=na.exclude)
car::Anova(HIF1A.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      23.0105  1  1.611e-06 ***
## Origin            0.0263  1     0.8712    
## Treatment         2.1843  3     0.5350    
## Origin:Treatment  2.0693  3     0.5582    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(HIF1A.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean    SE df lower.CL upper.CL
##  RF        146 10.55 19      124      169
##  RS        174  9.41 19      155      194
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      -28 13.4 19  -2.097  0.0496
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
HIF1A_sum<-summarySE(wgcna_counts_filtered_long_HIF1A , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HIF1A_sum
##   Origin Treatment2  N   Counts       sd        se       ci
## 1     RF     Stable 11 151.5455 28.24310  8.515615 18.97397
## 2     RF   Variable 11 141.2727 33.79672 10.190094 22.70494
## 3     RS     Stable 12 192.0833 51.70627 14.926313 32.85259
## 4     RS   Variable 12 165.1667 34.39565  9.929168 21.85395

Figure

pd<- position_dodge(0.2)
HIF1A_fig<-ggplot(data=HIF1A_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_HIF1A ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(HIF1A ~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
HIF1A_fig

HSP70

wgcna_counts_filtered_long_HSP70 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g10659.t1")
wgcna_counts_filtered_long_HSP70 
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               57     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               57     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               71     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               36     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               74     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               78     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               92     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               59     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               54     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               36     18 D         Stable    
## # ℹ 36 more rows
HSP70.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_HSP70 , na.action=na.exclude)
car::Anova(HSP70.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)   
## (Intercept)      10.4968  1   0.001196 **
## Origin            0.0701  1   0.791247   
## Treatment         1.9098  3   0.591333   
## Origin:Treatment  8.0987  3   0.044015 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(HSP70.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean    SE df lower.CL upper.CL
##  RF       63.1 10.78 19     40.5     85.6
##  RS      103.0  9.72 19     82.7    123.3
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      -40 13.1 19  -3.043  0.0067
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
HSP70_sum<-summarySE(wgcna_counts_filtered_long_HSP70 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
HSP70_sum
##   Origin Treatment2  N    Counts       sd        se       ci
## 1     RF     Stable 11  59.09091 15.70003  4.733737 10.54742
## 2     RF   Variable 11  71.63636 20.51474  6.185427 13.78199
## 3     RS     Stable 12 116.00000 64.30750 18.563976 40.85904
## 4     RS   Variable 12  95.33333 31.31463  9.039755 19.89637

Figure

pd<- position_dodge(0.2)
HSP70_fig<-ggplot(data=HSP70_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_HSP70 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(HSP70 ~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
HSP70_fig

PRKCD

wgcna_counts_filtered_long_PRKCD <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25259.t1")
wgcna_counts_filtered_long_PRKCD 
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                7     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D                9     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               47     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               18     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               25     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               62     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               22     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               35     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               28     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               23     18 D         Stable    
## # ℹ 36 more rows
PRKCD.lme <- lme(Counts~Origin*Treatment, random = ~1|Colony, data=wgcna_counts_filtered_long_PRKCD , na.action=na.exclude)
car::Anova(PRKCD.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                   Chisq Df Pr(>Chisq)   
## (Intercept)      9.0491  1   0.002628 **
## Origin           3.7499  1   0.052810 . 
## Treatment        5.4359  3   0.142525   
## Origin:Treatment 2.4700  3   0.480731   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(PRKCD.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF      25.40 3.25 19    18.60     32.2
##  RS       7.18 2.91 19     1.09     13.3
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     18.2 4.04 19   4.508  0.0002
## 
## Results are averaged over the levels of: Treatment 
## Degrees-of-freedom method: containment
library(Rmisc)
PRKCD_sum<-summarySE(wgcna_counts_filtered_long_PRKCD , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
PRKCD_sum
##   Origin Treatment2  N    Counts        sd       se        ci
## 1     RF     Stable 11 25.000000 17.487138 5.272571 11.748019
## 2     RF   Variable 11 26.636364 14.955084 4.509128 10.046962
## 3     RS     Stable 12  7.333333  7.749878 2.237197  4.924037
## 4     RS   Variable 12  7.083333  7.403419 2.137183  4.703908

Figure

pd<- position_dodge(0.2)
PRKCD_fig<-ggplot(data=PRKCD_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(PRKCD ~expression))+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
PRKCD_fig

compare_figs<-cowplot::plot_grid(SLC4A7_fig, NHE3_fig, CA1_fig, CA2_fig, HSP70_fig,HIF1A_fig, nrow=3)
compare_figs

Biomineralization toolkit present in modules

biomin <-read.csv("../../output/Biomin_blast_Pocillopora_acuta_best_hit.csv")
wgcnamod <-read.csv("../../output/WGCNA/WGCNA_ModuleMembership.csv")
wgcnamod<- plyr::rename(wgcnamod, c("X"="Pocillopora_acuta_best_hit"))
biomin_mod <- merge(biomin, wgcnamod, by=c("Pocillopora_acuta_best_hit"), all=F)
head(biomin_mod)
##                  Pocillopora_acuta_best_hit accessionnumber.geneID
## 1 Pocillopora_acuta_HIv2___RNAseq.g10093.t2         XP_022804785.1
## 2 Pocillopora_acuta_HIv2___RNAseq.g11609.t1              P33_g8985
## 3 Pocillopora_acuta_HIv2___RNAseq.g13172.t1             JR972076.1
## 4 Pocillopora_acuta_HIv2___RNAseq.g13172.t1            Gene:g13552
## 5 Pocillopora_acuta_HIv2___RNAseq.g13172.t1       aug_v2a.06327.t1
## 6 Pocillopora_acuta_HIv2___RNAseq.g13823.t1             PFX18785.1
##                                                          definition
## 1 thioredoxin reductase 1, cytoplasmic-like [Stylophora pistillata]
## 2                                      Flagellar associated protein
## 3              Acidic skeletal organic matrix protein (Acidic SOMP)
## 4                                     Acidic SOMP (Full-Length p27)
## 5                                                            SAARP3
## 6                                   Mucin-4 [Stylophora pistillata]
##                         Ref                              substanceBXH
## 1        Peled et al., 2020 Pocillopora_acuta_HIv2___RNAseq.g10093.t2
## 2        Drake et al., 2013 Pocillopora_acuta_HIv2___RNAseq.g11609.t1
## 3  Ramos-Silva et al., 2013 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 4 Mummadisetti et al., 2021 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 5     Takeuchi et al., 2016 Pocillopora_acuta_HIv2___RNAseq.g13172.t1
## 6        Peled et al., 2020 Pocillopora_acuta_HIv2___RNAseq.g13823.t1
##                           geneSymbol moduleColor
## 1 Pocillopora_acuta_HIv2___Sc0000021       brown
## 2 Pocillopora_acuta_HIv2___Sc0000013   turquoise
## 3 Pocillopora_acuta_HIv2___Sc0000004         red
## 4 Pocillopora_acuta_HIv2___Sc0000004         red
## 5 Pocillopora_acuta_HIv2___Sc0000004         red
## 6 Pocillopora_acuta_HIv2___Sc0000005        pink
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       GO.terms
## 1 GO:0000003,GO:0000302,GO:0000305,GO:0001650,GO:0001704,GO:0001707,GO:0001887,GO:0001890,GO:0003006,GO:0003674,GO:0003824,GO:0004791,GO:0005488,GO:0005515,GO:0005575,GO:0005622,GO:0005623,GO:0005634,GO:0005654,GO:0005730,GO:0005737,GO:0005739,GO:0005783,GO:0005829,GO:0006082,GO:0006139,GO:0006518,GO:0006520,GO:0006575,GO:0006725,GO:0006732,GO:0006733,GO:0006739,GO:0006749,GO:0006753,GO:0006790,GO:0006793,GO:0006796,GO:0006807,GO:0006950,GO:0006979,GO:0007154,GO:0007165,GO:0007275,GO:0007369,GO:0007498,GO:0008150,GO:0008152,GO:0008283,GO:0009056,GO:0009069,GO:0009117,GO:0009611,GO:0009628,GO:0009636,GO:0009653,GO:0009790,GO:0009888,GO:0009987,GO:0010035,GO:0010038,GO:0010269,GO:0010941,GO:0010942,GO:0012505,GO:0015036,GO:0015949,GO:0016043,GO:0016174,GO:0016209,GO:0016259,GO:0016491,GO:0016651,GO:0016667,GO:0016668,GO:0016999,GO:0017001,GO:0017144,GO:0018996,GO:0019216,GO:0019222,GO:0019362,GO:0019637,GO:0019725,GO:0019752,GO:0022404,GO:0022414,GO:0022607,GO:0023052,GO:0031974,GO:0031981,GO:0032501,GO:0032502,GO:0033554,GO:0033797,GO:0034599,GO:0034641,GO:0036295,GO:0036296,GO:0036477,GO:0042221,GO:0042303,GO:0042395,GO:0042493,GO:0042537,GO:0042592,GO:0042737,GO:0042743,GO:0042744,GO:0042802,GO:0042803,GO:0043025,GO:0043167,GO:0043169,GO:0043226,GO:0043227,GO:0043228,GO:0043229,GO:0043231,GO:0043232,GO:0043233,GO:0043436,GO:0043603,GO:0043933,GO:0044085,GO:0044237,GO:0044238,GO:0044248,GO:0044281,GO:0044297,GO:0044422,GO:0044424,GO:0044428,GO:0044444,GO:0044446,GO:0044452,GO:0044464,GO:0045340,GO:0045454,GO:0046483,GO:0046496,GO:0046688,GO:0046872,GO:0046914,GO:0046983,GO:0048332,GO:0048513,GO:0048518,GO:0048522,GO:0048598,GO:0048608,GO:0048646,GO:0048678,GO:0048729,GO:0048731,GO:0048856,GO:0050664,GO:0050789,GO:0050794,GO:0050896,GO:0051186,GO:0051187,GO:0051259,GO:0051262,GO:0051716,GO:0055086,GO:0055093,GO:0055114,GO:0061458,GO:0065003,GO:0065007,GO:0065008,GO:0070013,GO:0070276,GO:0070482,GO:0070887,GO:0070995,GO:0071241,GO:0071248,GO:0071280,GO:0071453,GO:0071455,GO:0071704,GO:0071840,GO:0072524,GO:0072593,GO:0080090,GO:0097237,GO:0097458,GO:0098623,GO:0098625,GO:0098626,GO:0098754,GO:0098869,GO:1901360,GO:1901564,GO:1901605,GO:1901700,GO:1990748
## 2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            -
## 3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <NA>
## 4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <NA>
## 5                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <NA>
## 6                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         <NA>
##                             GO.description     GS.Flat    GS.Slope    p.GS.Flat
## 1 thioredoxin-disulfide reductase activity  0.57178848 -0.57178848 3.311055e-05
## 2                                        - -0.29586493  0.29586493 4.589336e-02
## 3                                     <NA>  0.35628512 -0.35628512 1.508700e-02
## 4                                     <NA>  0.35628512 -0.35628512 1.508700e-02
## 5                                     <NA>  0.35628512 -0.35628512 1.508700e-02
## 6                                     <NA> -0.05455251  0.05455251 7.187880e-01
##     p.GS.Slope    A.brown    p.A.brown  A.magenta  p.A.magenta      A.red
## 1 3.311055e-05  0.7005073 5.973619e-08 -0.3738439 1.048844e-02  0.2901298
## 2 4.589336e-02 -0.4291375 2.921081e-03  0.3115539 3.505853e-02 -0.3452015
## 3 1.508700e-02  0.4914202 5.241968e-04 -0.6288605 2.864308e-06  0.6673892
## 4 1.508700e-02  0.4914202 5.241968e-04 -0.6288605 2.864308e-06  0.6673892
## 5 1.508700e-02  0.4914202 5.241968e-04 -0.6288605 2.864308e-06  0.6673892
## 6 7.187880e-01  0.0972208 5.203783e-01 -0.3252127 2.743096e-02  0.3709019
##        p.A.red A.turquoise p.A.turquoise   A.purple   p.A.purple    A.green
## 1 5.047695e-02 -0.43323233  2.634293e-03  0.6984202 6.792759e-08  0.4574538
## 2 1.879503e-02  0.58815287  1.720729e-05 -0.1784560 2.353887e-01 -0.1306835
## 3 4.071016e-07 -0.13892006  3.571825e-01  0.1198762 4.274677e-01  0.2378899
## 4 4.071016e-07 -0.13892006  3.571825e-01  0.1198762 4.274677e-01  0.2378899
## 5 4.071016e-07 -0.13892006  3.571825e-01  0.1198762 4.274677e-01  0.2378899
## 6 1.116224e-02  0.08164806  5.895928e-01 -0.1391597 3.563456e-01  0.1614616
##     p.A.green A.lightcyan p.A.lightcyan     A.pink     p.A.pink      A.blue
## 1 0.001391986  -0.3508191  1.682948e-02  0.1707384 2.565893e-01  0.12358439
## 2 0.386672688   0.1196505  4.283449e-01 -0.1522331 3.125037e-01 -0.58598406
## 3 0.111386103  -0.6473842  1.159989e-06  0.7188738 1.835918e-08  0.07448551
## 4 0.111386103  -0.6473842  1.159989e-06  0.7188738 1.835918e-08  0.07448551
## 5 0.111386103  -0.6473842  1.159989e-06  0.7188738 1.835918e-08  0.07448551
## 6 0.283719167  -0.5276145  1.646022e-04  0.6417477 1.537006e-06 -0.02286640
##       p.A.blue  A.salmon  p.A.salmon A.midnightblue p.A.midnightblue
## 1 4.132051e-01 0.1178467 0.435389343      0.2439890       0.10224333
## 2 1.880492e-05 0.1907995 0.204028320      0.2258109       0.13131383
## 3 6.227429e-01 0.4254256 0.003204458      0.2691022       0.07053914
## 4 6.227429e-01 0.4254256 0.003204458      0.2691022       0.07053914
## 5 6.227429e-01 0.4254256 0.003204458      0.2691022       0.07053914
## 6 8.801027e-01 0.2940377 0.047315397      0.2906592       0.05003895
##       A.black  p.A.black      A.cyan  p.A.cyan    A.yellow   p.A.yellow
## 1 -0.28430645 0.05550307  0.04904562 0.7461773  0.05522073 0.7154873547
## 2 -0.18825739 0.21023361  0.07386502 0.6256510 -0.14392338 0.3399558326
## 3  0.09618758 0.52484209  0.16699226 0.2673276 -0.38010677 0.0091694889
## 4  0.09618758 0.52484209  0.16699226 0.2673276 -0.38010677 0.0091694889
## 5  0.09618758 0.52484209  0.16699226 0.2673276 -0.38010677 0.0091694889
## 6  0.02103556 0.88964060 -0.13338389 0.3768501 -0.46998526 0.0009821983
##        A.tan     p.A.tan
## 1  0.2648346 0.075293267
## 2  0.2613466 0.079363532
## 3 -0.2055446 0.170565420
## 4 -0.2055446 0.170565420
## 5 -0.2055446 0.170565420
## 6 -0.3805358 0.009084622
plyr::count(biomin_mod, "moduleColor")
##    moduleColor freq
## 1        black    3
## 2         blue   36
## 3        brown   17
## 4         cyan    2
## 5        green    3
## 6      magenta    1
## 7         pink    7
## 8          red   18
## 9       salmon    6
## 10         tan    3
## 11   turquoise   21
## 12      yellow   10

Format GO terms to remove dashes and quotes and separate by semicolons (replace , with ;) in GOs column

biomin_mod$GO.terms <- gsub(",", ";", biomin_mod$GO.terms)
biomin_mod$GO.terms <- gsub('"', "", biomin_mod$GO.terms)
biomin_mod$GO.terms <- gsub("-", NA, biomin_mod$GO.terms)

GO terms

### Generate vector with names of all genes 
ALL.vector <- c(geneInfo$gene_id)
### Generate length vector for all genes 
LENGTH.vector <- as.integer(geneInfo$Length)
ID.vector_biomin <- biomin_mod %>%
  #filter(moduleColor=="black")%>%
  #get_rows(.data[[module]]))%>%
  pull(Pocillopora_acuta_best_hit)

##Get a list of GO Terms for each module
GO.terms_biomin <- biomin_mod %>%
  #filter(moduleColor=="black")%>%
  #filter(get_rows(.data[[module]]))%>%
  dplyr::select(GO.terms,Pocillopora_acuta_best_hit)
##Format to have one goterm per row with gene ID repeated
split <- strsplit(as.character(GO.terms_biomin$GO.terms), ";") 
split2 <- data.frame(v1 = rep.int(GO.terms_biomin$Pocillopora_acuta_best_hit, sapply(split, length)), v2 = unlist(split)) 
colnames(split2) <- c("Pocillopora_acuta_best_hit", "GO.terms")
GO.terms_biomin<-split2
head(GO.terms_biomin)
##                  Pocillopora_acuta_best_hit   GO.terms
## 1 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000003
## 2 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000302
## 3 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0000305
## 4 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001650
## 5 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001704
## 6 Pocillopora_acuta_HIv2___RNAseq.g10093.t2 GO:0001707
#GO.terms_biomin_sub <- GO.terms_biomin%>%
  #filter(Pocillopora_acuta_best_hit==c("Pocillopora_acuta_HIv2___RNAseq.g15280.t1","Pocillopora_acuta_HIv2___RNAseq.g7402.t1"))
#GO.terms_biomin_sub
##Construct list of genes with 1 for genes in module and 0 for genes not in the module
gene.vector=as.integer(ALL.vector %in% ID.vector) 
names(gene.vector)<-ALL.vector#set names
#weight gene vector by bias for length of gene 
pwf<-nullp(gene.vector, ID.vector, bias.data=LENGTH.vector) 
## Warning in pcls(G): initial point very close to some inequality constraints

#run goseq using Wallenius method for all categories of GO terms 
GO.wall<-goseq(pwf, ID.vector, gene2cat=GO.terms_biomin, test.cats=c("GO:BP", "GO:MF", "GO:CC"), method="Wallenius", use_genes_without_cat=TRUE)
## Using manually entered categories.
## Calculating the p-values...
## 'select()' returned 1:1 mapping between keys and columns
GO <- GO.wall[order(GO.wall$over_represented_pvalue),]
colnames(GO)[1] <- "GOterm"
#adjust p-values 
GO$bh_adjust <-  p.adjust(GO$over_represented_pvalue, method="BH") #add adjusted p-values
#Filtering for p < 0.01
GO <- GO %>%
        #dplyr::filter(bh_adjust<0.05) %>%
        dplyr::arrange(., ontology, bh_adjust)
   
#Write file of results 
write.csv(GO, file = "../../output/WGCNA/GO_analysis/goseq_pattern_biomin.csv")
#add vector for terms of interest to reduce number of GO terms - NOT using this to look at individual modules for exploratory purposes
keywords<-c("metabolism", "carbon","bicarbonate", "apoptosis", "death", "symbiosis", "regulation of cell communication", "trans membrane transport", "transmembrane",  "organic substance transport", "inorganic substance transport","response to stress", "antioxidant", "calcification","biomineralization", "heat","transporters","proton transport","ion transport","acid-base", "homeostasis")
go_results <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_biomin.csv")
 go_results<-go_results%>%
      filter(ontology=="BP")%>%
      filter(bh_adjust != "NA") %>%
      #filter(numInCat>10)%>%
      arrange(., bh_adjust)

head(go_results)
##   X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0006325             0.009864096                        1          2
## 2 2 GO:0016570             0.009864096                        1          2
## 3 3 GO:0051276             0.009864096                        1          2
## 4 4 GO:0060537             0.012664490                        1          2
## 5 5 GO:0000278             0.012973357                        1          2
## 6 6 GO:0007049             0.012973357                        1          2
##   numInCat                      term ontology bh_adjust
## 1        2    chromatin organization       BP 0.8862339
## 2        2      histone modification       BP 0.8862339
## 3        2   chromosome organization       BP 0.8862339
## 4        2 muscle tissue development       BP 0.8862339
## 5        2        mitotic cell cycle       BP 0.8862339
## 6        2                cell cycle       BP 0.8862339
library(rrvgo)
#Reduce/collapse GO term set with the rrvgo package 
simMatrix <- calculateSimMatrix(go_results$GOterm,
                                orgdb="org.Ce.eg.db", #c. elegans database
                                ont="BP",
                                method="Rel")
## preparing gene to GO mapping data...
## preparing IC data...
#calculate similarity 
scores <- setNames(-log(go_results$bh_adjust), go_results$GOterm)
reducedTerms <- reduceSimMatrix(simMatrix,
                                scores,
                                threshold=0.7,
                                orgdb="org.Ce.eg.db")
head(reducedTerms)
##                    go cluster     parent     score size
## GO:0030097 GO:0030097      30 GO:0030097 0.1207744    0
## GO:0002376 GO:0002376      25 GO:0002376 0.1207744   24
## GO:0032501 GO:0032501      19 GO:0032501 0.1207744    2
## GO:0044260 GO:0044260       7 GO:0044260 0.1207744   12
## GO:0006338 GO:0006338       1 GO:0006338 0.1207744   59
## GO:0008210 GO:0008210      26 GO:0008210 0.1207744    2
##                                                term
## GO:0030097                              hemopoiesis
## GO:0002376                    immune system process
## GO:0032501         multicellular organismal process
## GO:0044260 cellular macromolecule metabolic process
## GO:0006338                     chromatin remodeling
## GO:0008210               estrogen metabolic process
##                                          parentTerm termUniqueness
## GO:0030097                              hemopoiesis      0.9252500
## GO:0002376                    immune system process      1.0000000
## GO:0032501         multicellular organismal process      0.9398698
## GO:0044260 cellular macromolecule metabolic process      0.9288776
## GO:0006338                     chromatin remodeling      0.9418763
## GO:0008210               estrogen metabolic process      0.9288750
##            termUniquenessWithinCluster termDispensability
## GO:0030097                   1.0000000                  0
## GO:0002376                   1.0000000                  0
## GO:0032501                   0.6136250                  0
## GO:0044260                   0.6100000                  0
## GO:0006338                   0.5873333                  0
## GO:0008210                   0.5872500                  0
#keep only the goterms from the reduced list
go_results<-go_results%>%
  filter(GOterm %in% reducedTerms$go)
 #add in parent terms to list of go terms 
go_results$ParentTerm<-reducedTerms$parentTerm[match(go_results$GOterm, reducedTerms$go)]

go_results<-go_results %>%
  mutate(Factor = "Biomin")
head(go_results)
##   X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1 1 GO:0006325             0.009864096                        1          2
## 2 2 GO:0016570             0.009864096                        1          2
## 3 3 GO:0051276             0.009864096                        1          2
## 4 4 GO:0060537             0.012664490                        1          2
## 5 5 GO:0000278             0.012973357                        1          2
## 6 6 GO:0007049             0.012973357                        1          2
##   numInCat                      term ontology bh_adjust
## 1        2    chromatin organization       BP 0.8862339
## 2        2      histone modification       BP 0.8862339
## 3        2   chromosome organization       BP 0.8862339
## 4        2 muscle tissue development       BP 0.8862339
## 5        2        mitotic cell cycle       BP 0.8862339
## 6        2                cell cycle       BP 0.8862339
##                                                  ParentTerm Factor
## 1                                      chromatin remodeling Biomin
## 2                                 macromolecule deacylation Biomin
## 3                                      chromatin remodeling Biomin
## 4                                 muscle tissue development Biomin
## 5 microtubule cytoskeleton organization involved in mitosis Biomin
## 6 microtubule cytoskeleton organization involved in mitosis Biomin
write.csv(go_results, "../../output/Biomineralization_goterms.csv")
go_results<-go_results%>%
      filter(grepl(paste(keywords, collapse="|"), ParentTerm))
#plot significantly enriched GO terms by Slim Category faceted by slim term 
 GO.plot_biomin <-  ggplot(go_results, aes(x = ontology, y = term)) + 
    geom_point(aes(size=bh_adjust)) + 
    scale_size(name="Over rep. p-value", trans="reverse", range=c(1,3))+
    facet_grid(ParentTerm ~ ., scales = "free", labeller = label_wrap_gen(width = 5, multi_line = TRUE))+
    theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"),
    strip.text.y = element_text(angle=0, size = 10),
    strip.text.x = element_text(size = 20),
    axis.text = element_text(size = 8),
    axis.title.x = element_blank(),
    axis.title.y = element_blank())
GO.plot_biomin

***thioredoxin reductase 1

wgcna_counts_filtered_long_g10093 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g10093.t2")
wgcna_counts_filtered_long_g10093  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               59     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               69     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               86     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               75     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               49     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               63     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               80     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               57     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               98     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               73     18 D         Stable    
## # ℹ 36 more rows
g10093.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g10093 , na.action=na.exclude)
car::Anova(g10093.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       215.5517  1  < 2.2e-16 ***
## Origin              7.8998  1   0.004944 ** 
## Treatment2          1.0425  1   0.307243    
## Origin:Treatment2   2.7791  1   0.095503 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g10093.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       66.5 3.57 19     59.0     73.9
##  RS       44.8 3.43 19     37.6     52.0
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     21.6 4.47 23   4.836  0.0001
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g10093_sum<-summarySE(wgcna_counts_filtered_long_g10093 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g10093_sum
##   Origin Treatment2  N   Counts       sd       se        ci
## 1     RF     Stable 11 63.09091 12.70004 3.829205  8.532000
## 2     RF   Variable 11 68.18182 19.91893 6.005782 13.381717
## 3     RS     Stable 12 48.50000 12.57342 3.629634  7.988770
## 4     RS   Variable 12 42.08333 12.44960 3.593889  7.910096

Figure

pd<- position_dodge(0.2)
g10093_fig<-ggplot(data=g10093_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g10093,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Thioredoxin~reductase~1~expression))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g10093_fig

***Flagellar associated protein

wgcna_counts_filtered_long_g11609 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g11609.t1")
wgcna_counts_filtered_long_g11609  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              270     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              210     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              227     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              162     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              252     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              234     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              252     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              143     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              164     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              209     18 D         Stable    
## # ℹ 36 more rows
g11609.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g11609 , na.action=na.exclude)
car::Anova(g11609.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       55.8226  1  7.932e-14 ***
## Origin            10.3843  1   0.001271 ** 
## Treatment2         2.7386  1   0.097951 .  
## Origin:Treatment2  9.2919  1   0.002302 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g11609.lme, list(pairwise ~ Treatment2:Origin), adjust = "tukey")
tukey3
## $`emmeans of Treatment2, Origin`
##  Treatment2 Origin emmean   SE df lower.CL upper.CL
##  Stable     RF        213 28.5 19      153      273
##  Variable   RF        274 28.5 19      214      334
##  Stable     RS        337 27.3 19      280      395
##  Variable   RS        243 27.3 19      186      300
## 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Treatment2, Origin`
##  1                         estimate   SE df t.ratio p.value
##  Stable RF - Variable RF      -60.9 36.8 23  -1.655  0.3694
##  Stable RF - Stable RS       -124.1 38.5 23  -3.222  0.0184
##  Stable RF - Variable RS      -29.7 38.5 23  -0.772  0.8664
##  Variable RF - Stable RS      -63.2 38.5 23  -1.641  0.3763
##  Variable RF - Variable RS     31.2 38.5 23   0.809  0.8494
##  Stable RS - Variable RS       94.4 35.2 23   2.679  0.0601
## 
## Degrees-of-freedom method: containment 
## P value adjustment: tukey method for comparing a family of 4 estimates
library(Rmisc)
g11609_sum<-summarySE(wgcna_counts_filtered_long_g11609 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g11609_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 216.3636  68.28803 20.58961 45.87652
## 2     RF   Variable 11 277.2727 116.48699 35.12215 78.25702
## 3     RS     Stable 12 335.0833 113.94772 32.89387 72.39893
## 4     RS   Variable 12 240.6667  69.27985 19.99937 44.01831

Figure

pd<- position_dodge(0.2)
g11609_fig<-ggplot(data=g11609_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g11609 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Flagellar~associated~protein~expression))+
  ggtitle(~turquoise)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g11609_fig

***Actin

wgcna_counts_filtered_long_g14505 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g14505.t1")
wgcna_counts_filtered_long_g14505  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B             1418     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D             1504     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             1744     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C             1503     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             1805     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D             1848     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             1405     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D             1124     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             1361     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             1045     18 D         Stable    
## # ℹ 36 more rows
g14505.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g14505 , na.action=na.exclude)
car::Anova(g14505.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       134.3187  1  < 2.2e-16 ***
## Origin              7.2528  1   0.007079 ** 
## Treatment2          0.4459  1   0.504290    
## Origin:Treatment2   2.6857  1   0.101254    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g14505.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       1631 95.6 19     1431     1832
##  RS       1919 91.6 19     1727     2110
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS     -287 132 23  -2.170  0.0406
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g14505_sum<-summarySE(wgcna_counts_filtered_long_g14505 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g14505_sum
##   Origin Treatment2  N   Counts       sd        se       ci
## 1     RF     Stable 11 1567.545 325.2954  98.08024 218.5364
## 2     RF   Variable 11 1695.273 508.7410 153.39119 341.7769
## 3     RS     Stable 12 2071.833 491.8505 141.98500 312.5069
## 4     RS   Variable 12 1765.583 441.5165 127.45483 280.5262

Figure

pd<- position_dodge(0.2)
g14505_fig<-ggplot(data=g14505_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g14505 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Actin~expression))+
  ggtitle(~turquoise)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g14505_fig

***CA2

wgcna_counts_filtered_long_CA2<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g13824.t1")
wgcna_counts_filtered_long_CA2
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              139     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              164     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              169     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              249     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              174     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              232     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              232     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              204     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              244     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              338     18 D         Stable    
## # ℹ 36 more rows
CA2.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_CA2, na.action=na.exclude)
car::Anova(CA2.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)        96.0895  1     <2e-16 ***
## Origin            100.2939  1     <2e-16 ***
## Treatment2          2.1871  1     0.1392    
## Origin:Treatment2   1.0620  1     0.3027    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(CA2.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF     155.76 15.6 19    123.1      188
##  RS      -8.88 15.2 19    -40.8       23
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      165 14.9 23  11.032  <.0001
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
CA2_sum<-summarySE(wgcna_counts_filtered_long_CA2, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA2_sum
##   Origin Treatment2  N     Counts        sd        se        ci
## 1     RF     Stable 11 150.181818 98.679094 29.752866 66.293518
## 2     RF   Variable 11 131.272727 70.816793 21.352066 47.575369
## 3     RS     Stable 12  10.583333  9.894519  2.856302  6.286678
## 4     RS   Variable 12   9.916667  8.106769  2.340223  5.150795

Figure

pd<- position_dodge(0.2)
CA2_fig<-ggplot(data=CA2_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_CA2,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CA2~expression))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
CA2_fig

Poly [ADP-ribose] polymerase 11

wgcna_counts_filtered_long_g14663 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g14663.t1a")
wgcna_counts_filtered_long_g14663  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               61     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               54     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               19     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               24     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               23     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               49     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               17     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               13     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               16     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               10     18 D         Stable    
## # ℹ 36 more rows
g14663.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g14663 , na.action=na.exclude)
car::Anova(g14663.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       14.3519  1  0.0001516 ***
## Origin             0.5329  1  0.4653839    
## Treatment2         0.0388  1  0.8437699    
## Origin:Treatment2  0.0181  1  0.8928700    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g14663.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       25.5 5.73 19     13.5     37.5
##  RS       32.8 5.52 19     21.3     44.4
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -7.28 7.13 23  -1.020  0.3184
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g14663_sum<-summarySE(wgcna_counts_filtered_long_g14663 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g14663_sum
##   Origin Treatment2  N   Counts       sd       se        ci
## 1     RF     Stable 11 28.09091 15.23453 4.593384 10.234697
## 2     RF   Variable 11 26.54545 14.34129 4.324063  9.634613
## 3     RS     Stable 12 33.75000 28.81958 8.319496 18.311087
## 4     RS   Variable 12 33.66667 30.09782 8.688492 19.123243

Figure

pd<- position_dodge(0.2)
g14663_fig<-ggplot(data=g14663_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Poly~"[ADP-ribose]"~polymerase~11~expression))+
  ggtitle(~turquoise)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g14663_fig

***solute carrier family 4 member gamma

wgcna_counts_filtered_long_g15280 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g15280.t1")
wgcna_counts_filtered_long_g15280  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               30     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               34     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               29     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               30     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               94     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               76     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               49     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               39     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               64     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               64     18 D         Stable    
## # ℹ 36 more rows
g15280.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g15280 , na.action=na.exclude)
car::Anova(g15280.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       86.8426  1  < 2.2e-16 ***
## Origin             8.6455  1   0.003279 ** 
## Treatment2         0.8180  1   0.365763    
## Origin:Treatment2  0.8151  1   0.366627    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g15280.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       51.8 4.22 19     43.0     60.7
##  RS       32.8 4.04 19     24.3     41.3
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS       19 5.84 23   3.255  0.0035
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g15280_sum<-summarySE(wgcna_counts_filtered_long_g15280 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g15280_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 55.63636 24.46333 7.375972 16.43469
## 2     RF   Variable 11 48.00000 20.63008 6.220202 13.85947
## 3     RS     Stable 12 31.33333 16.77299 4.841946 10.65705
## 4     RS   Variable 12 34.25000 16.87454 4.871259 10.72157

Figure

pd<- position_dodge(0.2)
g15280_fig<-ggplot(data=g15280_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g15280 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Solute~carrier~family~4~member~gamma~expression))+
  ggtitle(~pink)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g15280_fig

MAGUK p55 subfamily member 7-like

wgcna_counts_filtered_long_g15517 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g15517.t1")
wgcna_counts_filtered_long_g15517  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                0     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               14     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               27     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               15     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               12     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               14     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               15     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D                0     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B                9     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               29     18 D         Stable    
## # ℹ 36 more rows
g15517.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g15517 , na.action=na.exclude)
car::Anova(g15517.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       27.9449  1  1.248e-07 ***
## Origin             0.2433  1     0.6219    
## Treatment2         0.2642  1     0.6073    
## Origin:Treatment2  1.3103  1     0.2523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g15517.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       13.5 1.95 19     9.47     17.6
##  RS       14.8 1.86 19    10.85     18.6
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     -1.2 2.69 23  -0.447  0.6589
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g15517_sum<-summarySE(wgcna_counts_filtered_long_g15517 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g15517_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 14.54545  9.490665 2.861543 6.375916
## 2     RF   Variable 11 12.54545 10.103105 3.046201 6.787358
## 3     RS     Stable 12 12.66667  7.992421 2.307213 5.078142
## 4     RS   Variable 12 16.83333  8.912028 2.572681 5.662432

Figure

pd<- position_dodge(0.2)
g15517_fig<-ggplot(data=g15517_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(MAGUK~p55~subfamily~member~7-like~expression))+
  ggtitle(~cyan)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g15517_fig

CARP1

wgcna_counts_filtered_long_g16280 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g16280.t1")
wgcna_counts_filtered_long_g16280  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              103     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              165     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              384     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              232     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              309     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              396     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              225     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              228     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              187     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              112     18 D         Stable    
## # ℹ 36 more rows
g16280.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g16280 , na.action=na.exclude)
car::Anova(g16280.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       68.2145  1     <2e-16 ***
## Origin             0.1913  1     0.6619    
## Treatment2         0.0758  1     0.7831    
## Origin:Treatment2  0.0179  1     0.8934    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g16280.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        276 26.3 19      221      331
##  RS        260 25.2 19      207      313
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     16.1 34.7 23   0.463  0.6477
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g16280_sum<-summarySE(wgcna_counts_filtered_long_g16280 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g16280_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 287.2727 124.33752 37.48917 83.53108
## 2     RF   Variable 11 275.2727  90.98691 27.43359 61.12584
## 3     RS     Stable 12 259.7500 119.95766 34.62879 76.21746
## 4     RS   Variable 12 255.8333 115.64824 33.38477 73.47939

Figure

pd<- position_dodge(0.2)
g16280_fig<-ggplot(data=g16280_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CARP1~expression))+
  ggtitle(~tan)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g16280_fig

PHD finger protein 21A-like

wgcna_counts_filtered_long_g1634 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g1634.t1")
wgcna_counts_filtered_long_g1634  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               65     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               80     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               90     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               79     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               95     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               95     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               76     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               78     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               91     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               79     18 D         Stable    
## # ℹ 36 more rows
g1634.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g1634 , na.action=na.exclude)
car::Anova(g1634.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       177.4960  1     <2e-16 ***
## Origin              0.5303  1     0.4665    
## Treatment2          0.4221  1     0.5159    
## Origin:Treatment2   0.0042  1     0.9480    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g1634.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       90.0 4.62 19     80.3     99.7
##  RS       96.2 4.42 19     86.9    105.4
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -6.17 6.39 23  -0.965  0.3448
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g1634_sum<-summarySE(wgcna_counts_filtered_long_g1634 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g1634_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 87.00000 11.13553 3.357488  7.48095
## 2     RF   Variable 11 93.00000 17.89413 5.395284 12.02144
## 3     RS     Stable 12 93.58333 25.61412 7.394161 16.27444
## 4     RS   Variable 12 98.75000 27.03911 7.805520 17.17983

Figure

pd<- position_dodge(0.2)
g1634_fig<-ggplot(data=g1634_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(PHD~finger~protein~"21A"~like~expression))+
  ggtitle(~turquoise)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g1634_fig

digestive cysteine proteinase 1-like

wgcna_counts_filtered_long_g18103 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g18103.t1")
wgcna_counts_filtered_long_g18103  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                0     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D                9     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B                0     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               12     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B                6     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D                0     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               19     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D                0     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               10     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               10     18 D         Stable    
## # ℹ 36 more rows
g18103.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g18103 , na.action=na.exclude)
car::Anova(g18103.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       14.6577  1  0.0001289 ***
## Origin             1.8257  1  0.1766413    
## Treatment2         0.8843  1  0.3470176    
## Origin:Treatment2  0.0057  1  0.9398383    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g18103.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF      10.14 1.60 19     6.80    13.47
##  RS       5.75 1.53 19     2.55     8.95
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     4.39 2.21 23   1.986  0.0590
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g18103_sum<-summarySE(wgcna_counts_filtered_long_g18103 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g18103_sum
##   Origin Treatment2  N    Counts        sd       se       ci
## 1     RF     Stable 11  8.636364  6.313046 1.903455 4.241162
## 2     RF   Variable 11 11.636364 10.032674 3.024965 6.740042
## 3     RS     Stable 12  4.416667  7.216878 2.083333 4.585386
## 4     RS   Variable 12  7.083333  5.822501 1.680811 3.699440

Figure

pd<- position_dodge(0.2)
g18103_fig<-ggplot(data=g18103_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g18103 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(digestive~cysteine~proteinase~1-like~expression))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g18103_fig

endothelin-converting enzyme 1-like isoform X2

wgcna_counts_filtered_long_g19211 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g19211.t1")
wgcna_counts_filtered_long_g19211  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               46     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               56     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               51     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               30     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               43     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               27     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               37     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               47     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               54     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               67     18 D         Stable    
## # ℹ 36 more rows
g19211.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g19211 , na.action=na.exclude)
car::Anova(g19211.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       87.3886  1    < 2e-16 ***
## Origin             3.4868  1    0.06186 .  
## Treatment2         0.1819  1    0.66975    
## Origin:Treatment2  0.8990  1    0.34305    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g19211.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       48.7 3.93 19     40.5     56.9
##  RS       57.2 3.77 19     49.3     65.1
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -8.49 5.15 23  -1.647  0.1131
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g19211_sum<-summarySE(wgcna_counts_filtered_long_g19211 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g19211_sum
##   Origin Treatment2  N   Counts       sd       se        ci
## 1     RF     Stable 11 48.18182 12.97550 3.912261  8.717060
## 2     RF   Variable 11 50.90909 12.33251 3.718393  8.285096
## 3     RS     Stable 12 59.16667 22.09415 6.378032 14.037954
## 4     RS   Variable 12 53.50000 17.65065 5.095304 11.214688

Figure

pd<- position_dodge(0.2)
g19211_fig<-ggplot(data=g19211_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(endothelin-converting~enzyme~1-like~isoform~X2~expression))+
  ggtitle(~green)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g19211_fig

chymotrypsin-like elastase family member 1

wgcna_counts_filtered_long_g19288 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g19288.t1")
wgcna_counts_filtered_long_g19288  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                0     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D                0     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B                0     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C                0     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B                0     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               48     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B                0     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               25     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               29     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               52     18 D         Stable    
## # ℹ 36 more rows
g19288.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g19288 , na.action=na.exclude)
car::Anova(g19288.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                    Chisq Df Pr(>Chisq)   
## (Intercept)       6.7903  1   0.009165 **
## Origin            0.0148  1   0.903331   
## Treatment2        2.4178  1   0.119960   
## Origin:Treatment2 0.0599  1   0.806669   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g19288.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       9.58 4.58 19  -0.0019     19.2
##  RS      11.54 4.40 19   2.3230     20.8
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -1.96 5.73 23  -0.342  0.7357
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g19288_sum<-summarySE(wgcna_counts_filtered_long_g19288 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g19288_sum
##   Origin Treatment2  N    Counts        sd       se        ci
## 1     RF     Stable 11 13.272727 20.337605 6.132019 13.662989
## 2     RF   Variable 11  3.363636  8.834848 2.663807  5.935332
## 3     RS     Stable 12 15.833333 25.785244 7.443559 16.383162
## 4     RS   Variable 12  8.083333 15.264089 4.406363  9.698340

Figure

pd<- position_dodge(0.2)
g19288_fig<-ggplot(data=g19288_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(chymotrypsin-like~elastase~family~member~1~expression))+
  ggtitle(~cyan)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g19288_fig

CUB and peptidase domain-containing protein 2-like

wgcna_counts_filtered_long_g21338 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g21338.t1")
wgcna_counts_filtered_long_g21338  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                6     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D                0     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B                7     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C                9     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               25     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               20     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B                7     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D                0     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               15     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               32     18 D         Stable    
## # ℹ 36 more rows
g21338.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21338 , na.action=na.exclude)
car::Anova(g21338.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       19.7037  1  9.043e-06 ***
## Origin             0.8360  1     0.3605    
## Treatment2         0.0175  1     0.8947    
## Origin:Treatment2  2.2965  1     0.1297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g21338.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       14.3 2.66 19     8.71     19.8
##  RS       14.3 2.56 19     8.99     19.7
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS  -0.0721 3.24 23  -0.022  0.9824
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g21338_sum<-summarySE(wgcna_counts_filtered_long_g21338 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21338_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 13.00000 11.063453 3.335757 7.432529
## 2     RF   Variable 11 13.45455  7.353416 2.217138 4.940092
## 3     RS     Stable 12 17.83333 13.354150 3.855011 8.484822
## 4     RS   Variable 12 11.08333 10.422514 3.008720 6.622149

Figure

pd<- position_dodge(0.2)
g21338_fig<-ggplot(data=g21338_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CUB~and~peptidase~domain-containing~protein~2-like~expression))+
  ggtitle(~cyan)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g21338_fig

##L-type calcium channel alpha-1 subunit

wgcna_counts_filtered_long_g21501 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g21501.t1")
wgcna_counts_filtered_long_g21501  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               28     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               29     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               19     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               16     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               31     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               28     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B                0     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               23     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B                0     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D                0     18 D         Stable    
## # ℹ 36 more rows
g21501.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21501 , na.action=na.exclude)
car::Anova(g21501.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       33.2008  1  8.312e-09 ***
## Origin             0.5237  1     0.4693    
## Treatment2         0.4276  1     0.5132    
## Origin:Treatment2  0.1305  1     0.7179    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g21501.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       20.3 2.71 19     14.6     26.0
##  RS       17.8 2.60 19     12.4     23.3
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     2.48 3.75 23   0.662  0.5145
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g21501_sum<-summarySE(wgcna_counts_filtered_long_g21501 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21501_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 22.09091 10.02452 3.022505 6.734561
## 2     RF   Variable 11 18.54545 12.91792 3.894900 8.678379
## 3     RS     Stable 12 18.25000 14.12364 4.077144 8.973734
## 4     RS   Variable 12 17.41667 13.22160 3.816746 8.400601

Figure

pd<- position_dodge(0.2)
g21501_fig<-ggplot(data=g21501_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(L-type~calcium~channel~alpha-1~subunit~expression))+
  ggtitle(~turquoise)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g21501_fig

##.Protocadherin

wgcna_counts_filtered_long_g21501 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g22388.t1")
wgcna_counts_filtered_long_g21501  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              133     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              138     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              163     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              156     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              164     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              181     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              130     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              185     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              157     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              144     18 D         Stable    
## # ℹ 36 more rows
g21501.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g21501 , na.action=na.exclude)
car::Anova(g21501.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       302.6581  1     <2e-16 ***
## Origin              1.4899  1     0.2222    
## Treatment2          0.7306  1     0.3927    
## Origin:Treatment2   0.3360  1     0.5621    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g21501.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        162 6.38 19      149      176
##  RS        142 6.11 19      129      155
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     20.4 8.83 23   2.306  0.0305
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g21501_sum<-summarySE(wgcna_counts_filtered_long_g21501 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g21501_sum
##   Origin Treatment2  N   Counts       sd        se       ci
## 1     RF     Stable 11 157.0000 20.72679  6.249364 13.92445
## 2     RF   Variable 11 167.9091 28.94980  8.728693 19.44874
## 3     RS     Stable 12 141.7500 37.05064 10.695599 23.54085
## 4     RS   Variable 12 142.4167 29.92250  8.637882 19.01185

Figure

pd<- position_dodge(0.2)
g21501_fig<-ggplot(data=g21501_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g21501 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Protocadherin~expression), limits=c(100,200))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g21501_fig
## Warning: Removed 5 rows containing missing values (`geom_point()`).

##Acropora yongei Na+/Ca2+ exchanger

wgcna_counts_filtered_long_g24639 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g24639.t1")
wgcna_counts_filtered_long_g24639  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               70     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               79     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               66     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              108     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               58     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               94     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               90     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               60     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               62     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               62     18 D         Stable    
## # ℹ 36 more rows
g24639.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g24639 , na.action=na.exclude)
car::Anova(g24639.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       208.9868  1     <2e-16 ***
## Origin              0.0585  1     0.8089    
## Treatment2          0.0158  1     0.8999    
## Origin:Treatment2   0.6123  1     0.4339    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g24639.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       80.6 4.21 19     71.8     89.4
##  RS       78.5 4.03 19     70.1     86.9
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS      2.1 5.6 23   0.375  0.7115
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g24639_sum<-summarySE(wgcna_counts_filtered_long_g24639 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g24639_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 81.27273 15.07376 4.544909 10.12669
## 2     RF   Variable 11 82.18182 20.54175 6.193572 13.80014
## 3     RS     Stable 12 81.58333 16.05365 4.634290 10.20000
## 4     RS   Variable 12 74.66667 21.21035 6.122900 13.47641

Figure

pd<- position_dodge(0.2)
g24639_fig<-ggplot(data=g24639_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Acropora~yongei~"Na+/Ca2+"~exchanger~expression))+
  ggtitle(~tan)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g24639_fig

##**mammalian ependymin-related protein 1-like

wgcna_counts_filtered_long_g25351 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25351.t1")
wgcna_counts_filtered_long_g25351  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B             5677     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D             5022     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B            13830     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C             5729     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             5012     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D             8437     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             7397     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D             6199     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             5018     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             4565     18 D         Stable    
## # ℹ 36 more rows
g25351.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g25351 , na.action=na.exclude)
car::Anova(g25351.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       113.2918  1    < 2e-16 ***
## Origin              6.5397  1    0.01055 *  
## Treatment2          2.9203  1    0.08747 .  
## Origin:Treatment2   1.6792  1    0.19503    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g25351.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean  SE df lower.CL upper.CL
##  RF       6638 396 19     5809     7466
##  RS       3944 379 19     3151     4738
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS     2693 548 23   4.912  0.0001
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g25351_sum<-summarySE(wgcna_counts_filtered_long_g25351 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g25351_sum
##   Origin Treatment2  N   Counts        sd       se        ci
## 1     RF     Stable 11 5960.909 1716.8516 517.6502 1153.3966
## 2     RF   Variable 11 7314.364 2894.5978 872.7541 1944.6172
## 3     RS     Stable 12 3978.167  792.8993 228.8903  503.7842
## 4     RS   Variable 12 3910.750 1499.1418 432.7650  952.5093

Figure

pd<- position_dodge(0.2)
g25351_fig<-ggplot(data=g25351_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g25351,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(mammalian~ependymin-related~protein~1-like~expression), limits=c(2500,12500))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g25351_fig
## Warning: Removed 2 rows containing missing values (`geom_point()`).

##MAM and LDLr domain-containing protein

wgcna_counts_filtered_long_g25935 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g25935.t1")
wgcna_counts_filtered_long_g25935  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B             1097     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              929     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             1262     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C             1089     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             1127     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D             1375     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             1184     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D             1275     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             1207     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             1525     18 D         Stable    
## # ℹ 36 more rows
g25935.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g25935 , na.action=na.exclude)
car::Anova(g25935.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       274.9500  1     <2e-16 ***
## Origin              2.0555  1     0.1517    
## Treatment2          0.1990  1     0.6555    
## Origin:Treatment2   0.4517  1     0.5015    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g25935.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       1324 59.6 19     1199     1449
##  RS       1216 57.1 19     1096     1335
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate SE df t.ratio p.value
##  RF - RS      108 81 23   1.338  0.1941
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g25935_sum<-summarySE(wgcna_counts_filtered_long_g25935 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g25935_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 1344.182 302.1115 91.09005 202.9613
## 2     RF   Variable 11 1294.818 214.8175 64.76992 144.3164
## 3     RS     Stable 12 1183.250 328.6159 94.86323 208.7926
## 4     RS   Variable 12 1236.833 214.0076 61.77867 135.9739

Figure

pd<- position_dodge(0.2)
g25935_fig<-ggplot(data=g25935_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(MAM~and~LDLr~domain-containing~protein~expression))+
  ggtitle(~brown)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g25935_fig

SLIT-ROBO Rho GTPase-activating protein 1-like

wgcna_counts_filtered_long_g27376 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27376.t1")
wgcna_counts_filtered_long_g27376  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               35     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               28     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               40     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               67     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               19     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               38     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               31     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               39     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               38     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               26     18 D         Stable    
## # ℹ 36 more rows
g27376.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27376 , na.action=na.exclude)
car::Anova(g27376.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       98.9685  1     <2e-16 ***
## Origin             0.0141  1     0.9054    
## Treatment2         0.2753  1     0.5998    
## Origin:Treatment2  0.7895  1     0.3742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g27376.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       36.0 2.72 19     30.4     41.7
##  RS       38.6 2.60 19     33.2     44.1
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -2.58 3.72 23  -0.694  0.4945
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g27376_sum<-summarySE(wgcna_counts_filtered_long_g27376 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27376_sum
##   Origin Treatment2  N   Counts        sd       se        ci
## 1     RF     Stable 11 37.36364 10.763575 3.245340  7.231068
## 2     RF   Variable 11 34.63636 11.209574 3.379814  7.530694
## 3     RS     Stable 12 36.83333 16.889121 4.875469 10.730836
## 4     RS   Variable 12 40.50000  9.491623 2.739996  6.030690

Figure

pd<- position_dodge(0.2)
g27376_fig<-ggplot(data=g27376_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(SLIT-ROBO~Rho~GTPase-activating~protein~1-like~expression))+
  ggtitle(~grey60)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g27376_fig

##**Hephaestin-like protein - frontloaded

wgcna_counts_filtered_long_g27566 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27566.t1")
wgcna_counts_filtered_long_g27566  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               12     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               24     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               15     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               21     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               30     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               26     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               15     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               15     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B                0     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               17     18 D         Stable    
## # ℹ 36 more rows
g27566.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27566 , na.action=na.exclude)
car::Anova(g27566.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       56.1260  1  6.797e-14 ***
## Origin             6.3687  1    0.01162 *  
## Treatment2         0.0303  1    0.86179    
## Origin:Treatment2  0.0252  1    0.87392    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g27566.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       19.0 1.83 19    15.22     22.9
##  RS       10.4 1.75 19     6.75     14.1
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     8.63 2.53 23   3.410  0.0024
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g27566_sum<-summarySE(wgcna_counts_filtered_long_g27566 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27566_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 19.36364 8.139690 2.454209 5.468318
## 2     RF   Variable 11 18.72727 8.978763 2.707199 6.032015
## 3     RS     Stable 12 10.33333 9.528267 2.750574 6.053972
## 4     RS   Variable 12 10.50000 7.501515 2.165501 4.766235

Figure

pd<- position_dodge(0.2)
g27566_fig<-ggplot(data=g27566_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g27566 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Hephaestin-like~protein~expression))+
  ggtitle(~brown)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g27566_fig

plasma membrane calcium ATPase - frontloaded

wgcna_counts_filtered_long_g27976 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g27976.t1")
wgcna_counts_filtered_long_g27976  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              197     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              219     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              204     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              124     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              174     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              155     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              183     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              173     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              122     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              223     18 D         Stable    
## # ℹ 36 more rows
g27976.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27976 , na.action=na.exclude)
car::Anova(g27976.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       209.0712  1     <2e-16 ***
## Origin              0.0008  1     0.9774    
## Treatment2          0.3269  1     0.5675    
## Origin:Treatment2   0.0012  1     0.9725    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g27976.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        184 8.74 19      166      202
##  RS        184 8.37 19      166      201
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS   0.0682 12.1 23   0.006  0.9956
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g27976_sum<-summarySE(wgcna_counts_filtered_long_g27976 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27976_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 178.8182 39.85177 12.01576 26.77278
## 2     RF   Variable 11 188.8182 33.30111 10.04066 22.37199
## 3     RS     Stable 12 178.3333 45.44794 13.11969 28.87624
## 4     RS   Variable 12 189.1667 43.65950 12.60341 27.73992

Figure

pd<- position_dodge(0.2)
g27976_fig<-ggplot(data=g27976_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(plasma~membrane~calcium~ATPase~expression))+
  ggtitle(~brown)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g27976_fig

von Willebrand factor D and EGF domain-containing protein-like

wgcna_counts_filtered_long_g28226 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g28226.t2")
wgcna_counts_filtered_long_g28226  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                0     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D                0     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B                0     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C                0     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               66     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              103     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B                0     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D                0     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B                0     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D                0     18 D         Stable    
## # ℹ 36 more rows
g28226.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g28226 , na.action=na.exclude)
car::Anova(g28226.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       13.4863  1  0.0002403 ***
## Origin             6.2703  1  0.0122776 *  
## Treatment2         2.3220  1  0.1275576    
## Origin:Treatment2  3.0108  1  0.0827128 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g28226.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       30.2 8.97 19    11.43     49.0
##  RS       16.6 8.82 19    -1.88     35.1
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     13.6 7.17 23   1.899  0.0702
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g28226_sum<-summarySE(wgcna_counts_filtered_long_g28226 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g28226_sum
##   Origin Treatment2  N   Counts       sd        se       ci
## 1     RF     Stable 11 35.18182 57.46968 17.327759 38.60865
## 2     RF   Variable 11 26.36364 40.63317 12.251362 27.29774
## 3     RS     Stable 12 13.08333 16.41761  4.739355 10.43125
## 4     RS   Variable 12 18.16667 25.95392  7.492252 16.49034

Figure

pd<- position_dodge(0.2)
g28226_fig<-ggplot(data=g28226_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g28226,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(von~Willebrand~factor~D~and~EGF~domain-containing~protein-like~expression), limits=c(0,120))+
  ggtitle(~magenta)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g28226_fig
## Warning: Removed 1 rows containing missing values (`geom_point()`).

**Cephalotoxin-like protein

wgcna_counts_filtered_long_g5013 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g5013.t1")
wgcna_counts_filtered_long_g5013  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B                6     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               14     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B               14     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C                6     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               23     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D                9     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               16     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               21     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               22     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               12     18 D         Stable    
## # ℹ 36 more rows
g5013.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g5013 , na.action=na.exclude)
car::Anova(g5013.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       44.8432  1  2.135e-11 ***
## Origin             6.0215  1    0.01413 *  
## Treatment2         0.2947  1    0.58719    
## Origin:Treatment2  0.0002  1    0.99022    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g5013.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF      13.76 1.41 19    10.80     16.7
##  RS       7.22 1.35 19     4.39     10.0
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     6.54 1.93 23   3.393  0.0025
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g5013_sum<-summarySE(wgcna_counts_filtered_long_g5013 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g5013_sum
##   Origin Treatment2  N    Counts       sd       se       ci
## 1     RF     Stable 11 13.090909 7.063350 2.129680 4.745223
## 2     RF   Variable 11 14.545455 6.170310 1.860419 4.145271
## 3     RS     Stable 12  6.416667 5.583390 1.611786 3.547517
## 4     RS   Variable 12  7.916667 6.934215 2.001735 4.405790

Figure

pd<- position_dodge(0.2)
g5013_fig<-ggplot(data=g5013_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g5013 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(cephalotoxin-like~expression))+
  ggtitle(~blue)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g5013_fig

**sodium bicarbonate cotransporter 3-like isoform X - SLC4A7

wgcna_counts_filtered_long_g7402 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7402.t1")
wgcna_counts_filtered_long_g7402  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              391     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              785     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              328     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              522     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              799     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              863     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              557     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              533     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              906     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              818     18 D         Stable    
## # ℹ 36 more rows
g7402.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g7402, na.action=na.exclude)
car::Anova(g7402.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       102.5059  1    < 2e-16 ***
## Origin              6.4414  1    0.01115 *  
## Treatment2          1.0268  1    0.31091    
## Origin:Treatment2   0.6016  1    0.43798    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g7402.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        660 49.6 19      556      764
##  RS        467 47.5 19      367      566
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS      193 68.7 23   2.814  0.0099
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g7402_sum<-summarySE(wgcna_counts_filtered_long_g7402 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g7402_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 710.3636 265.8632 80.16078 178.6093
## 2     RF   Variable 11 609.8182 241.3930 72.78272 162.1700
## 3     RS     Stable 12 463.8333 248.6454 71.77773 157.9817
## 4     RS   Variable 12 469.8333 166.4407 48.04730 105.7514

Figure

pd<- position_dodge(0.2)
g7402_fig<-ggplot(data=g7402_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g7402 ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(sodium~bicarbonate~cotransporter~3-like~isoform~X2~expression))+
  ggtitle(~pink)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g7402_fig

##*protein lingerer-like

wgcna_counts_filtered_long_g7902 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___RNAseq.g7908.t1")
wgcna_counts_filtered_long_g7902  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               51     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               73     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              101     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               79     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              107     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               95     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               61     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               75     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               92     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               68     18 D         Stable    
## # ℹ 36 more rows
g7902.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g7902, na.action=na.exclude)
car::Anova(g7902.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       167.9194  1  < 2.2e-16 ***
## Origin             11.0673  1  0.0008786 ***
## Treatment2          0.0009  1  0.9759807    
## Origin:Treatment2   0.0806  1  0.7764657    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g7902.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       82.9 4.53 19     73.4     92.3
##  RS       51.6 4.34 19     42.5     60.7
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     31.3 6.27 23   4.989  <.0001
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g7902_sum<-summarySE(wgcna_counts_filtered_long_g7902 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g7902_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 83.00000 24.55606 7.403930 16.49698
## 2     RF   Variable 11 82.72727 21.99587 6.632004 14.77702
## 3     RS     Stable 12 53.50000 19.52853 5.637402 12.40784
## 4     RS   Variable 12 49.66667 18.80683 5.429065 11.94929

Figure

pd<- position_dodge(0.2)
g7902_fig<-ggplot(data=g7902_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_g7902,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(protein~lingerer-like~expression))+
  ggtitle(~pink)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g7902_fig

***CA1

wgcna_counts_filtered_long_CA1<- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g12304.t1")
wgcna_counts_filtered_long_CA1
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B             2854     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D             4635     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             2949     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C             4681     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B             7704     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D             8665     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B             3948     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D             3887     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B             6896     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             6597     18 D         Stable    
## # ℹ 36 more rows
CA1.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_CA1, na.action=na.exclude)
car::Anova(CA1.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       97.1566  1  < 2.2e-16 ***
## Origin            15.7334  1  7.293e-05 ***
## Treatment2         2.1565  1      0.142    
## Origin:Treatment2  1.3573  1      0.244    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(CA1.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean  SE df lower.CL upper.CL
##  RF       5346 434 19     4437     6256
##  RS       2716 416 19     1846     3587
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate  SE df t.ratio p.value
##  RF - RS     2630 597 23   4.408  0.0002
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
CA1_sum<-summarySE(wgcna_counts_filtered_long_CA1, measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
CA1_sum
##   Origin Treatment2  N   Counts       sd       se        ci
## 1     RF     Stable 11 5970.636 2621.084 790.2864 1760.8679
## 2     RF   Variable 11 4733.455 1986.062 598.8204 1334.2549
## 3     RS     Stable 12 2653.583 1847.177 533.2340 1173.6400
## 4     RS   Variable 12 2775.250 1463.636 422.5154  929.9501

Figure

pd<- position_dodge(0.2)
CA1_fig<-ggplot(data=CA1_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  geom_point(data=wgcna_counts_filtered_long_CA1,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(CA1~expression))+
  ggtitle(~pink)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
CA1_fig

##Uncharacterized skeletal organic matrix protein-6 (USOMP6)

wgcna_counts_filtered_long_g22622 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g22622.t1")
wgcna_counts_filtered_long_g22622  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              902     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              733     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B             1136     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              857     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              558     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              726     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              774     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              888     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              617     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D             1117     18 D         Stable    
## # ℹ 36 more rows
g22622.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g22622, na.action=na.exclude)
car::Anova(g22622.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       155.9337  1     <2e-16 ***
## Origin              0.0013  1     0.9712    
## Treatment2          0.5378  1     0.4633    
## Origin:Treatment2   0.7739  1     0.3790    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g22622.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        903 54.2 19      790     1016
##  RS        846 51.9 19      738      955
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate SE df t.ratio p.value
##  RF - RS     56.9 71 23   0.801  0.4313
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g22622_sum<-summarySE(wgcna_counts_filtered_long_g22622 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g22622_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 870.9091 172.5407 52.02298 115.9144
## 2     RF   Variable 11 935.3636 251.5207 75.83634 168.9739
## 3     RS     Stable 12 859.9167 294.4059 84.98765 187.0566
## 4     RS   Variable 12 817.3333 190.8728 55.10023 121.2748

Figure

pd<- position_dodge(0.2)
g22622_fig<-ggplot(data=g22622_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Uncharacterized~skeletal~organic~matrix~protein-6~(USOMP6)~expression))+
  ggtitle(~green)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g22622_fig

##band 3 anion transport protein-like

wgcna_counts_filtered_long_g27873 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g27873.t1")
wgcna_counts_filtered_long_g27873  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               52     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               65     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              105     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               52     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               22     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               50     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               44     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               94     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               58     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               60     18 D         Stable    
## # ℹ 36 more rows
g27873.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g27873, na.action=na.exclude)
car::Anova(g27873.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       109.0383  1     <2e-16 ***
## Origin              0.7687  1     0.3806    
## Treatment2          0.0156  1     0.9006    
## Origin:Treatment2   0.2677  1     0.6049    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g27873.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       71.1 5.15 19     60.3     81.8
##  RS       59.9 4.94 19     49.6     70.3
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     11.1 6.84 23   1.628  0.1171
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g27873_sum<-summarySE(wgcna_counts_filtered_long_g27873 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g27873_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 70.63636 21.65767 6.530032 14.54982
## 2     RF   Variable 11 71.72727 27.65896 8.339491 18.58154
## 3     RS     Stable 12 61.50000 22.71763 6.558016 14.43410
## 4     RS   Variable 12 56.33333 17.55166 5.066726 11.15179

Figure

pd<- position_dodge(0.2)
g27873_fig<-ggplot(data=g27873_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(band~3~anion~transport~protein-like~expression))+
  ggtitle(~green)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g27873_fig

##collagenase 3-like

wgcna_counts_filtered_long_g5338 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g5338.t1")
wgcna_counts_filtered_long_g5338  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B               15     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D               14     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B                9     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C               35     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B               22     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D               16     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B               24     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D               30     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B               21     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D               22     18 D         Stable    
## # ℹ 36 more rows
g5338.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g5338, na.action=na.exclude)
car::Anova(g5338.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                     Chisq Df Pr(>Chisq)    
## (Intercept)       64.2427  1    1.1e-15 ***
## Origin             0.7901  1     0.3741    
## Treatment2         0.0652  1     0.7985    
## Origin:Treatment2  0.0281  1     0.8670    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g5338.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF       20.6 1.78 19     16.9     24.4
##  RS       17.1 1.70 19     13.6     20.7
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS     3.51 2.46 23   1.425  0.1677
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g5338_sum<-summarySE(wgcna_counts_filtered_long_g5338 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g5338_sum
##   Origin Treatment2  N   Counts        sd       se       ci
## 1     RF     Stable 11 20.18182  7.180782 2.165087 4.824115
## 2     RF   Variable 11 21.09091  7.955558 2.398691 5.344617
## 3     RS     Stable 12 17.08333 10.202124 2.945100 6.482120
## 4     RS   Variable 12 17.16667  7.601834 2.194460 4.829975

Figure

pd<- position_dodge(0.2)
g5338_fig<-ggplot(data=g5338_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(collagenase~3-like~expression))+
  ggtitle(~green)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g5338_fig

Protocadherin

wgcna_counts_filtered_long_g6583 <- wgcna_counts_filtered_long %>%
  filter(Gene == "Pocillopora_acuta_HIv2___TS.g6583.t1")
wgcna_counts_filtered_long_g6583  
## # A tibble: 46 × 7
##    Gene                  Origin Colony.number Counts Colony Treatment Treatment2
##    <chr>                 <fct>  <chr>          <int>  <dbl> <fct>     <fct>     
##  1 Pocillopora_acuta_HI… RF     13B              254     13 B         Variable  
##  2 Pocillopora_acuta_HI… RF     13D              287     13 D         Stable    
##  3 Pocillopora_acuta_HI… RF     14B              305     14 B         Variable  
##  4 Pocillopora_acuta_HI… RF     14C              229     14 C         Stable    
##  5 Pocillopora_acuta_HI… RF     15B              320     15 B         Variable  
##  6 Pocillopora_acuta_HI… RF     15D              241     15 D         Stable    
##  7 Pocillopora_acuta_HI… RF     17B              171     17 B         Variable  
##  8 Pocillopora_acuta_HI… RF     17D              261     17 D         Stable    
##  9 Pocillopora_acuta_HI… RF     18B              256     18 B         Variable  
## 10 Pocillopora_acuta_HI… RF     18D              274     18 D         Stable    
## # ℹ 36 more rows
g6583.lme <- lme(Counts~Origin*Treatment2, random = ~1|Colony, data=wgcna_counts_filtered_long_g6583, na.action=na.exclude)
car::Anova(g6583.lme, type=3)
## Analysis of Deviance Table (Type III tests)
## 
## Response: Counts
##                      Chisq Df Pr(>Chisq)    
## (Intercept)       245.3182  1     <2e-16 ***
## Origin              1.5732  1     0.2097    
## Treatment2          0.0685  1     0.7935    
## Origin:Treatment2   0.5712  1     0.4498    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
tukey3<- emmeans(g6583.lme, list(pairwise ~ Origin), adjust = "tukey")
## NOTE: Results may be misleading due to involvement in interactions
tukey3
## $`emmeans of Origin`
##  Origin emmean   SE df lower.CL upper.CL
##  RF        283 13.2 19      256      311
##  RS        302 12.6 19      275      328
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment 
## Confidence level used: 0.95 
## 
## $`pairwise differences of Origin`
##  1       estimate   SE df t.ratio p.value
##  RF - RS    -18.3 18.1 23  -1.014  0.3210
## 
## Results are averaged over the levels of: Treatment2 
## Degrees-of-freedom method: containment
library(Rmisc)
g6583_sum<-summarySE(wgcna_counts_filtered_long_g6583 , measurevar='Counts', groupvars=c('Origin', 'Treatment2'), na.rm=TRUE, conf.interval = 0.95)
g6583_sum
##   Origin Treatment2  N   Counts       sd       se       ci
## 1     RF     Stable 11 286.9091 43.36463 13.07493 29.13275
## 2     RF   Variable 11 280.2727 48.05224 14.48830 32.28194
## 3     RS     Stable 12 318.0000 80.29491 23.17914 51.01695
## 4     RS   Variable 12 284.8333 61.77648 17.83333 39.25090

Figure

pd<- position_dodge(0.2)
g6583_fig<-ggplot(data=g6583_sum, aes(y=Counts, x=Treatment2, color=Origin),group = interaction(Origin))+
  #geom_point(data=wgcna_counts_filtered_long_PRKCD ,aes(y=Counts, x=Treatment2, color=Origin), alpha=0.4, position = pd)+
  geom_line(aes(group = interaction(Origin), stat="identity"),position=position_dodge(0.2))+
  geom_point(size=3, stat="identity", position = pd)+
  geom_errorbar(aes(ymin=Counts-se, ymax=Counts+se), stat="identity",width=0.2, position = pd)+
  scale_fill_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_color_manual("Origin", values=c("RS"='paleturquoise3', "RF"= "indianred"))+
  scale_y_continuous(expression(Protocadherin~expression))+
  ggtitle(~black)+
  theme_classic()+
  theme(axis.text.x=element_text(vjust=0.5,size=12),#angling the labels on the x-axis
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),#telling it where to position our plot title
        panel.background= element_rect(fill=NA, color='black'),#this is making the black box around the graph
        #strip.background = element_blank(), 
        #strip.text = element_blank(),
        legend.title = element_text(vjust=0.5,size=12),
        legend.position="none",
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_text(size=12))#making the axis title larger 
## Warning in geom_line(aes(group = interaction(Origin), stat = "identity"), :
## Ignoring unknown aesthetics: stat
g6583_fig

Comparison of sig genes

biomin_compare_figs<-cowplot::plot_grid(g10093_fig,CA2_fig,g25351_fig,g5013_fig,g15280_fig, CA1_fig,g7402_fig,g7902_fig,g27566_fig,g28226_fig,g14505_fig,g11609_fig,g10093_fig, nrow=4)
## Warning: Removed 2 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
biomin_compare_figs

Merge the frequency of GOterms for both up- and down-regulation of calcification

cal_biomin_terms <-read.csv("../../output/Biomineralization_goterms.csv") 
head(cal_biomin_terms)
##   X.1 X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1   1 1 GO:0006325             0.009864096                        1          2
## 2   2 2 GO:0016570             0.009864096                        1          2
## 3   3 3 GO:0051276             0.009864096                        1          2
## 4   4 4 GO:0060537             0.012664490                        1          2
## 5   5 5 GO:0000278             0.012973357                        1          2
## 6   6 6 GO:0007049             0.012973357                        1          2
##   numInCat                      term ontology bh_adjust
## 1        2    chromatin organization       BP 0.8862339
## 2        2      histone modification       BP 0.8862339
## 3        2   chromosome organization       BP 0.8862339
## 4        2 muscle tissue development       BP 0.8862339
## 5        2        mitotic cell cycle       BP 0.8862339
## 6        2                cell cycle       BP 0.8862339
##                                                  ParentTerm Factor
## 1                                      chromatin remodeling Biomin
## 2                                 macromolecule deacylation Biomin
## 3                                      chromatin remodeling Biomin
## 4                                 muscle tissue development Biomin
## 5 microtubule cytoskeleton organization involved in mitosis Biomin
## 6 microtubule cytoskeleton organization involved in mitosis Biomin
cal_up_terms <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_filtered.csv")
cal_up_terms<- cal_up_terms %>%
  mutate(Factor = "Up")
head(cal_up_terms)
##   X.1 X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1   1 1 GO:0000003                       0                        1        465
## 2   2 2 GO:0006139                       0                        1        608
## 3   3 3 GO:0006355                       0                        1        462
## 4   4 4 GO:0006725                       0                        1        650
## 5   5 5 GO:0006807                       0                        1       1215
## 6   6 6 GO:0006810                       0                        1        664
##   numInCat                                             term ontology bh_adjust
## 1      465                                     reproduction       BP         0
## 2      608 nucleobase-containing compound metabolic process       BP         0
## 3      462        regulation of DNA-templated transcription       BP         0
## 4      650     cellular aromatic compound metabolic process       BP         0
## 5     1215              nitrogen compound metabolic process       BP         0
## 6      664                                        transport       BP         0
##                           ParentTerm Factor
## 1                       reproduction     Up
## 2                  metabolic process     Up
## 3 regulation of biosynthetic process     Up
## 4                  metabolic process     Up
## 5                  metabolic process     Up
## 6                       localization     Up
cal_down_terms <-read.csv("../../output/WGCNA/GO_analysis/goseq_pattern_calcification_down_filtered.csv")
cal_down_terms<-cal_down_terms %>%
  mutate(Factor = "Down")
head(cal_down_terms)
##   X.1 X     GOterm over_represented_pvalue under_represented_pvalue numDEInCat
## 1   1 1 GO:0006139                       0                        1        485
## 2   2 2 GO:0006725                       0                        1        513
## 3   3 3 GO:0006807                       0                        1        910
## 4   4 4 GO:0006810                       0                        1        419
## 5   5 5 GO:0006950                       0                        1        364
## 6   6 6 GO:0006996                       0                        1        461
##   numInCat                                             term ontology bh_adjust
## 1      485 nucleobase-containing compound metabolic process       BP         0
## 2      513     cellular aromatic compound metabolic process       BP         0
## 3      910              nitrogen compound metabolic process       BP         0
## 4      419                                        transport       BP         0
## 5      364                               response to stress       BP         0
## 6      461                           organelle organization       BP         0
##                                      ParentTerm Factor
## 1                               gene expression   Down
## 2                               gene expression   Down
## 3                               gene expression   Down
## 4                                  localization   Down
## 5                            response to stress   Down
## 6 cellular component organization or biogenesis   Down
colnames(cal_biomin_terms)
##  [1] "X.1"                      "X"                       
##  [3] "GOterm"                   "over_represented_pvalue" 
##  [5] "under_represented_pvalue" "numDEInCat"              
##  [7] "numInCat"                 "term"                    
##  [9] "ontology"                 "bh_adjust"               
## [11] "ParentTerm"               "Factor"
colnames(cal_up_terms)
##  [1] "X.1"                      "X"                       
##  [3] "GOterm"                   "over_represented_pvalue" 
##  [5] "under_represented_pvalue" "numDEInCat"              
##  [7] "numInCat"                 "term"                    
##  [9] "ontology"                 "bh_adjust"               
## [11] "ParentTerm"               "Factor"
colnames(cal_down_terms)
##  [1] "X.1"                      "X"                       
##  [3] "GOterm"                   "over_represented_pvalue" 
##  [5] "under_represented_pvalue" "numDEInCat"              
##  [7] "numInCat"                 "term"                    
##  [9] "ontology"                 "bh_adjust"               
## [11] "ParentTerm"               "Factor"

Merge biomineralization, up and down-regulation of calcification GOterms

all_terms<- merge(cal_up_terms,cal_down_terms, by=c("Factor","GOterm","X.1","X","GOterm","over_represented_pvalue","under_represented_pvalue","numDEInCat","numInCat","term","ontology","bh_adjust","ParentTerm"),all=T)


all_terms<- merge(all_terms,cal_biomin_terms, by=c("Factor","GOterm","X.1","X","GOterm","over_represented_pvalue","under_represented_pvalue","numDEInCat","numInCat","term","ontology","bh_adjust","ParentTerm"),all=T)

all_terms$GOterm<-as.factor(all_terms$GOterm)
head(all_terms)
##   Factor     GOterm X.1   X over_represented_pvalue under_represented_pvalue
## 1 Biomin GO:0000003 300 343              0.54128075                0.8456150
## 2 Biomin GO:0000041 417 565              1.00000000                0.8535542
## 3 Biomin GO:0000122  65  70              0.12290664                1.0000000
## 4 Biomin GO:0000132 107 122              0.15108190                1.0000000
## 5 Biomin GO:0000226 256 298              0.37841205                0.9418666
## 6 Biomin GO:0000278   5   5              0.01297336                1.0000000
##   numDEInCat numInCat                                                      term
## 1          1        5                                              reproduction
## 2          0        1                            transition metal ion transport
## 3          1        1 negative regulation of transcription by RNA polymerase II
## 4          1        1              establishment of mitotic spindle orientation
## 5          1        3                     microtubule cytoskeleton organization
## 6          2        2                                        mitotic cell cycle
##   ontology bh_adjust                                                ParentTerm
## 1       BP 1.0000000                                female sex differentiation
## 2       BP 1.0000000                                        calcium ion import
## 3       BP 0.8862339                 negative regulation of biological process
## 4       BP 0.8862339              establishment of mitotic spindle orientation
## 5       BP 1.0000000                                 microtubule-based process
## 6       BP 0.8862339 microtubule cytoskeleton organization involved in mitosis
tail(all_terms)
##      Factor     GOterm  X.1    X over_represented_pvalue
## 4614     Up GO:2001242  922  983            5.023572e-31
## 4615     Up GO:2001243 1342 1500            2.171774e-18
## 4616     Up GO:2001251  978 1045            3.318296e-29
## 4617     Up GO:2001252  713  759            1.527374e-42
## 4618     Up GO:2001257 1514 1737            2.346995e-15
## 4619     Up GO:2001259 2050 2535            2.136874e-09
##      under_represented_pvalue numDEInCat numInCat
## 4614                        1         42       42
## 4615                        1         24       24
## 4616                        1         43       43
## 4617                        1         61       61
## 4618                        1         21       21
## 4619                        1         12       12
##                                                              term ontology
## 4614          regulation of intrinsic apoptotic signaling pathway       BP
## 4615 negative regulation of intrinsic apoptotic signaling pathway       BP
## 4616               negative regulation of chromosome organization       BP
## 4617               positive regulation of chromosome organization       BP
## 4618                        regulation of cation channel activity       BP
## 4619               positive regulation of cation channel activity       BP
##         bh_adjust                                    ParentTerm
## 4614 5.462041e-30                      regulation of cell death
## 4615 1.558880e-17                      regulation of cell death
## 4616 3.389807e-28 regulation of cellular component organization
## 4617 2.152863e-41 regulation of cellular component organization
## 4618 1.467562e-14                    regulation of localization
## 4619 9.175464e-09                    regulation of localization
goterms_shared <- all_terms %>%
  group_by(GOterm) %>%
  dplyr::summarise(
    ParentTerm = paste(unique(ParentTerm), collapse = ", "),
    Factor = paste(unique(Factor), collapse = ", ")
  )
dim(goterms_shared)
## [1] 2322    3
write.csv(goterms_shared, "../../output/WGCNA/GO_analysis/Merged_GOterms_factor_ParentTerm.csv")
result_unique <- goterms_shared %>%
  group_by(ParentTerm,Factor) %>%
  dplyr::summarise(SharedGOterms = n_distinct(GOterm))%>%
  arrange(-SharedGOterms)
## `summarise()` has grouped output by 'ParentTerm'. You can override using the
## `.groups` argument.
result_filtered_up<- result_unique %>%
  dplyr::filter(Factor=="Biomin, Down, Up" | Factor=="Biomin, Up")
 #dplyr::filter(SharedGOterms>=5) 
result_filtered_down<- result_unique %>%
  dplyr::filter(Factor=="Biomin, Down, Up" | Factor=="Biomin, Down")
 #dplyr::filter(SharedGOterms>=5) 

Calculate the percentage of shared GOterms upregulation of calcification

merged_up <- result_filtered_up %>%
  tidyr::separate_rows(ParentTerm, sep = ", ") %>%
  dplyr::group_by(ParentTerm) %>%
  dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE))%>%
  left_join(result, by = "ParentTerm") %>%
  mutate(Proportion.of.shared.GO.terms.with.biomineralization.genes = Sum_of_SharedGOterms / Number.of.terms)%>%
  mutate(Percentage.of.shared.GO.terms.with.biomineralization.genes = Proportion.of.shared.GO.terms.with.biomineralization.genes * 100)
merged_up_clean <- na.omit(merged_up)
merged_up_clean
## # A tibble: 106 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              12 Up                    
##  2 aging                                1               2 Up                    
##  3 amide metabolic …                    2              12 Up                    
##  4 ammonium ion met…                    1               1 Up                    
##  5 anatomical struc…                   24              27 Up                    
##  6 animal organ dev…                   12              15 Up                    
##  7 behavior                             3              10 Up                    
##  8 biosynthetic pro…                    1              25 Up                    
##  9 carbohydrate der…                    4              27 Up                    
## 10 catabolic process                   15              39 Up                    
## # ℹ 96 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Calculate the percentage of shared GOterms downregulation of calcification

merged_down <- result_filtered_down %>%
  tidyr::separate_rows(ParentTerm, sep = ", ") %>%
  dplyr::group_by(ParentTerm) %>%
  dplyr::summarize(Sum_of_SharedGOterms = sum(SharedGOterms, na.rm = TRUE))%>%
  left_join(result_down, by = "ParentTerm") %>%
  mutate(Proportion.of.shared.GO.terms.with.biomineralization.genes = Sum_of_SharedGOterms / Number.of.terms)%>%
  mutate(Percentage.of.shared.GO.terms.with.biomineralization.genes = Proportion.of.shared.GO.terms.with.biomineralization.genes * 100)
merged_down_clean<- na.omit(merged_down)
merged_down_clean
## # A tibble: 96 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              11 Down                  
##  2 aging                                1               2 Down                  
##  3 amide metabolic …                    2              12 Down                  
##  4 ammonium ion met…                    1               1 Down                  
##  5 anatomical struc…                   23              29 Down                  
##  6 animal organ dev…                   12              16 Down                  
##  7 behavior                             3               8 Down                  
##  8 biosynthetic pro…                    1              24 Down                  
##  9 carbohydrate der…                    4              18 Down                  
## 10 catabolic process                   15              38 Down                  
## # ℹ 86 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Frequency >10 up

cal_freq_terms_filtered_up <- merged_up_clean %>%
  filter(Number.of.terms>=10) %>%
  filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up
## # A tibble: 71 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              12 Up                    
##  2 amide metabolic …                    2              12 Up                    
##  3 anatomical struc…                   24              27 Up                    
##  4 animal organ dev…                   12              15 Up                    
##  5 behavior                             3              10 Up                    
##  6 biosynthetic pro…                    1              25 Up                    
##  7 carbohydrate der…                    4              27 Up                    
##  8 catabolic process                   15              39 Up                    
##  9 cell cycle                           9              63 Up                    
## 10 cell division                        4              14 Up                    
## # ℹ 61 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes,group=1))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
freq_fig_up

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up.png", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)
## Saving 7 x 12 in image

Frequency >10 down

cal_freq_terms_filtered_down <- merged_down_clean %>%
  filter(Number.of.terms>=10)
  #filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down
## # A tibble: 56 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              11 Down                  
##  2 amide metabolic …                    2              12 Down                  
##  3 anatomical struc…                   23              29 Down                  
##  4 animal organ dev…                   12              16 Down                  
##  5 biosynthetic pro…                    1              24 Down                  
##  6 carbohydrate der…                    4              18 Down                  
##  7 catabolic process                   15              38 Down                  
##  8 cell cycle                           9              43 Down                  
##  9 cell differentia…                   11              12 Down                  
## 10 cell projection …                   15              20 Down                  
## # ℹ 46 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  #geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+ 
  #scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
freq_fig_down

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down.png", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)
## Saving 7 x 12 in image
compare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs

Frequency >20 up

cal_freq_terms_filtered_up <- merged_up_clean %>%
  filter(Number.of.terms>=20) %>%
  filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up
## # A tibble: 43 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 anatomical struc…                   24              27 Up                    
##  2 biosynthetic pro…                    1              25 Up                    
##  3 carbohydrate der…                    4              27 Up                    
##  4 catabolic process                   15              39 Up                    
##  5 cell cycle                           9              63 Up                    
##  6 cell projection …                   17              34 Up                    
##  7 cell surface rec…                    3              23 Up                    
##  8 cellular compone…                    9              29 Up                    
##  9 cellular compone…                    6              20 Up                    
## 10 cellular localiz…                    3              27 Up                    
## # ℹ 33 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes,group=1))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
freq_fig_up

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_gr20.png", plot=freq_fig_up, dpi=300, height=12, units="in", limitsize=FALSE)
## Saving 7 x 12 in image

Frequency >20 down

cal_freq_terms_filtered_down <- merged_down_clean %>%
  filter(Number.of.terms>=20)
  #filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down
## # A tibble: 29 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 anatomical struc…                   23              29 Down                  
##  2 biosynthetic pro…                    1              24 Down                  
##  3 catabolic process                   15              38 Down                  
##  4 cell cycle                           9              43 Down                  
##  5 cell projection …                   15              20 Down                  
##  6 cellular compone…                    9              25 Down                  
##  7 cellular compone…                    6              22 Down                  
##  8 cellular localiz…                    3              25 Down                  
##  9 homeostatic proc…                   12              22 Down                  
## 10 immune system pr…                    1              23 Down                  
## # ℹ 19 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

freq_fig_down<-ggplot(cal_freq_terms_filtered_down, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  #geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+ 
  #scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
freq_fig_down

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_gr20.png", plot=freq_fig_down, dpi=300, height=12, units="in", limitsize=FALSE)
## Saving 7 x 12 in image
compare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, nrow=2, align="v")
compare_figs

All terms up

cal_freq_terms_filtered_up_all <- merged_up_clean %>%
  filter(Calcification.direction=="Up")
cal_freq_terms_filtered_up_all
## # A tibble: 106 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              12 Up                    
##  2 aging                                1               2 Up                    
##  3 amide metabolic …                    2              12 Up                    
##  4 ammonium ion met…                    1               1 Up                    
##  5 anatomical struc…                   24              27 Up                    
##  6 animal organ dev…                   12              15 Up                    
##  7 behavior                             3              10 Up                    
##  8 biosynthetic pro…                    1              25 Up                    
##  9 carbohydrate der…                    4              27 Up                    
## 10 catabolic process                   15              39 Up                    
## # ℹ 96 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

#counts$Direction.of.flat.origin<- factor(counts$Direction.of.flat.origin, levels =c("up","no pattern","down"))
#counts$Module<- factor(counts$Module, levels=c("Blue","Brown","Greenyellow","Cyan","Pink","Magenta","Lightcyan","Midnight blue","Purple","Turquiose","Red","Black"))
freq_fig_up<-ggplot(cal_freq_terms_filtered_up_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes,group=1))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(0, 100))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95,size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
freq_fig_up

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_up_ALL.png", plot=freq_fig_up, dpi=300, height=17, units="in", limitsize=FALSE)
## Saving 7 x 17 in image

All terms down

cal_freq_terms_filtered_down_all <- merged_down_clean %>%
  filter(Calcification.direction=="Down")
cal_freq_terms_filtered_down_all
## # A tibble: 96 × 6
##    ParentTerm        Sum_of_SharedGOterms Number.of.terms Calcification.direct…¹
##    <chr>                            <int>           <int> <chr>                 
##  1 actin filament-b…                    5              11 Down                  
##  2 aging                                1               2 Down                  
##  3 amide metabolic …                    2              12 Down                  
##  4 ammonium ion met…                    1               1 Down                  
##  5 anatomical struc…                   23              29 Down                  
##  6 animal organ dev…                   12              16 Down                  
##  7 behavior                             3               8 Down                  
##  8 biosynthetic pro…                    1              24 Down                  
##  9 carbohydrate der…                    4              18 Down                  
## 10 catabolic process                   15              38 Down                  
## # ℹ 86 more rows
## # ℹ abbreviated name: ¹​Calcification.direction
## # ℹ 2 more variables:
## #   Proportion.of.shared.GO.terms.with.biomineralization.genes <dbl>,
## #   Percentage.of.shared.GO.terms.with.biomineralization.genes <dbl>

Figure

freq_fig_down<-ggplot(cal_freq_terms_filtered_down_all, aes(y=Number.of.terms,x=reorder(ParentTerm, Number.of.terms), fill=Percentage.of.shared.GO.terms.with.biomineralization.genes))+
  #facet_wrap(~Calcification.direction, nrow = 1)+
  geom_point(size=5, alpha=1, pch=21,color="black")+
  geom_segment(aes(x=ParentTerm, xend=ParentTerm, y=0, yend=Number.of.terms)) +
  #geom_hline(yintercept = 0, linetype="solid", color = 'black', size=0.5, show.legend = TRUE)+
  coord_flip()+
  scale_y_continuous(expression(GO~term~counts),limits=c(0,70))+
  #scale_color_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  #scale_fill_manual("Direction.of.flat.origin",values= c("up"="#b2182b","no pattern"="grey","down" ="#67a9cf"))+
  scale_fill_gradientn(colours=c("white","#d1e5f0","#92c5de","#4393c3","#2166ac"), na.value = "grey98",limits = c(0, 100))+ 
  #scale_fill_gradientn(colours=c("#fddbc7","#f4a582","#d6604d","#b2182b"), na.value = "grey98",limits = c(10, 40))+ 
 #scale_color_gradientn(colours=c("#b2182b","#fddbc7","white","#d1e5f0","#67a9cf", "#67a9cf", "#2166ac"), na.value = "grey98",limits = c(-0, 40))+ 
  theme_classic()+
   theme(axis.text.x=element_text(vjust=0.5, hjust=0.95, size=12),
        plot.title = element_text(margin = margin(t = 10, b = 10), hjust=0.5),
        panel.background= element_rect(fill=NA, color='black'),
        legend.title = element_blank(),
        axis.text.y = element_text(vjust=0.5, size=12), #making the axis text larger 
        axis.title.x = element_blank(),#making the axis title larger 
        axis.title.y = element_blank(),
        strip.text = element_text(size=12))#making the axis title larger 
freq_fig_down

ggsave(filename="../../output/WGCNA/GO_analysis/freq_fig_down_ALL.png", plot=freq_fig_down, dpi=300, height=17, units="in", limitsize=FALSE)
## Saving 7 x 17 in image
compare_figs<-cowplot::plot_grid(freq_fig_up, freq_fig_down, ncol=2, align="h")
compare_figs